Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Mathematical programming”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Verified Optimizer for Quantum Circuits

We present VOQC, the first verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called SQIR, a small quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of SQIR programs. SQIR programs denote complex-valued matrices, as is standard in quantum computation, but we treat matrices symbolically to reason about programs that use an arbitrary number of quantum bits. SQIR’s careful design and our provided automation make it possible to write and verify a broad range of optimizations in VOQC, including full-circuit transformations from cutting-edge optimizers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

GIS @ LANL

Why is GIS important to LANL, how is it being used, and what can the GIS Program do for you? The GIS Program is an institutional resource for all things related to GIS/spatial data/mapping. We have a robust Enterprise GIS platform with over 25 years worth of LANL operational spatial data. We are constantly making ties to additional tabular data sources to integrate information so that it can be leveraged through interactive mapping applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning

A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Basic Energy Sciences Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The EIC on a Table Top (Final Report)

A research collaboration between JLab and the Atomic and Molecular Optics group of Olivier Pfister at UVA was established to adapt and enhance their optics-based Quantum Computing (QC) system for the calculation of Doubly-Deeply Virtual Compton Scattering amplitudes relevant for JLab12 and the EIC program. The formalism and simulation infrastructure for computing matrix elements of such Compton amplitudes was established. In partnership with UVA, JLab has provided the technical expertise to enhance the current system, and explored significantly scaling it to become a unique and powerful resource.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Automating the Analysis of Large Language Models Responses through Zero-Shot Question Answering

Recent advancements in Large Language Models (LLMs) have shown significant potential in various applications, yet their evaluation, particularly in zero-shot question answering scenarios, remains a challenging task. In this study, our objective was to explore precision metrics for Large Language Models (LLM) and design and implement a software pipeline to automatically evaluate LLMs' outputs under zero-shot question answering. Zero-shot question answering involves a model providing answers to questions about topics it hasn't seen during training. It leverages the principles of zero-shot learning by relying on semantic understanding and generalization from related knowledge. The data used was metadata from medical databases on congenital heart disease. We explored eleven LLM metrics and selected three for our evaluation: BLEU, BERTScore, and MoverScore. BLEU calculates a score based on the overlap of n-grams (contiguous sequences of n items, typically words) between the machine-generated translation and the reference translations. Higher BLEU scores indicate better correspondence between the machine-generated and human-generated translations. BERTScore is a metric used to evaluate the quality of machine-generated text by measuring the similarity of token embeddings produced by BERT (Bidirectional Encoder Representations from Transformers) between the generated text and reference text. MoverScore is a metric that quantifies the dissimilarity between the distributions of word embeddings from machine-generated text and reference text, emphasizing semantic similarity over exact token overlap. We also introduced HBKI, a composite metric summarizing these approaches. We tested five models —GPT-3, Llama-2, Gemini 1.5 Pro, Solar 10.7B, and Mixtral-8x7b. Our software pipeline, designed and implemented using Object-Oriented Programming principles, allows users to customize the selection and extraction of features for topics of interest in their own research. Our results show that MoverScore delivered the most precise evaluation of the LLM's outputs, while Mixtral-8x7b achieved the best overall performance in extracting metadata from the databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Simulator for Neyer Tests of Explosives

Explosives and explosive devices such as detonators are typically tested by applying a range of stimuli such as voltage or mechanical shock, and recording binary “detonated/did not detonate” responses. These are analyzed using maximum likelihood or generalized linear models to provide estimates of quantities such as the all-fire and no-fire points. Given that the true threshold for detonation is unknown a priori , sequential design methods are typically used to optimize the set of test points. One popular method, implemented in commercial software, is Neyer’s algorithm. To support simulation and experimental design, we have developed code in the R programming language to duplicate the functions of the Neyer software. We provide code for the simulator along with a description and examples of usage.

42 ENGINEERING↗
DOE OSTI2020

Systems, methods and computer program products for constructing complex geometries using layered and linked hexahedral element meshes

Systems, methods and computer program products for creating 3D representations of bodies are disclosed. The systems, methods and computer program products include the construction of FE meshes representing complex geometries. The complex geometries may be artificially or naturally formed or designed geometries. The techniques reduce the number of elements as much as possible to save on computer run time while maintaining computational accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reducing the cost of n modular redundancy for neural networks

An N modular redundancy method, system, and computer program product include a computer-implemented N modular redundancy method for neural networks, the method including selectively replicating the neural network by employing one of checker neural networks and selective N modular redundancy (N-MR) applied only to critical computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

IBR Digital Supply Chain Gap Analysis and Recommendations

The adoption of clean energy technologies, including solar photovoltaics, continues to introduce non-traditional stakeholders to the operations and planning of the electric system. Stakeholders such as manufacturers, vendors, owners, aggregators, and others are enabling the adoption, integration, and optimum operations of solar technologies at accelerated rates. Inverters form the foundation of many digitally controlled energy sources for clean energy technologies, including Solar, Battery Energy Storage Systems, Hybrid Systems, and Hydrogen Fuel Cells. Their supply chain is complex, a series of microchips, electronic switches and other components making up its primary functions. The complexity of this space and the growing digitization associated with these components can create supply chain cyber risks. One measure to mitigate cybersecurity attacks is proper digital supply chain security. The U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO), in partnership with the Cybersecurity, Energy, Security, and Emergency Response (CESER) office, is hosting a workshop to bring together solar vendors and services providers to discuss digital supply chain security for solar systems and challenges and opportunities in the transitioning to a fully domestic supply chain for solar energy in the U.S. This workshop will support the Securing Solar for the Grid (S2G) and Energy Cyber Sense program activities. During the workshop, industry experts and researchers from DOE National Laboratories will discuss the current solar supply chain landscape and the transition to domestic manufacturing of solar components in the U.S. Tools and techniques to better manage and secure the digital supply chain of solar devices and systems will be discussed.

cybersecurity↗
DOE OSTI2024

Transforming Energy Through Computational Excellence: NREL HPC Resources for High Performance Computing for Energy Innovation (HPC4EI) Program

NREL hosts computing facilities for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In 2024, NREL introduced Kestrel, the 3rd generation, EERE-sponsored supercomputer dedicated to renewable energy and energy efficiency research. Kestrel has already been used for hundreds of research projects by NREL, other national laboratories, and university partners. This includes HPC4EI-sponsored industrial partnerships.

high-performance computing↗
DOE OSTI2024

Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems

In previous work, we have introduced a mathematical model for solving a discretized version of the process family design problem. This involves two sets of decision variables. One set selects which unit module designs are included in the process platform out of a candidate set of options; the other set determines which of these unit module designs are assigned to each variant. In this work, we exploit a parallelized Progressive Hedging (PH) algorithm to solve even larger scale design problems. PH is a well-known algorithm traditionally used to solve stochastic programming problems. While our problem is not a two-stage stochastic programming problem, the structure is similar, and it can be directly mapped to the PH approach, which we employ here to solve this deterministic optimization problem. We decompose our problem by process variant. We treat the platform unit module design variables as first-stage and the assignment of unit module designs to variants as second-stage, solving the problem using mpi-sppy. We demonstrate this approach on case studies of CC, water desalination, and refrigeration.

Stinchfield, Georgia↗
DOE OSTI2025

Integrating Energy-Efficient Computing with Computational Research to Accelerate Energy Technology

NREL's computational sciences center hosts the largest high performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Analytic Benchmark for the Solution to the Isotopic Fission Spectrum Mixture Problem

In neutron transport calculations, it is common to specify material compositions in terms of constituent isotopes. Material compositions may be described by isotope number densities and associated microscopic cross sections. For general reaction cross sections, the macroscopic cross sections of a composition are simply the summation of the sum of the products of isotope number densities and microscopic cross sections. A notable exception is the mixture of the neutron fission spectrum in fissile material. To demonstrate proper and improper mixture of the neutron fission spectrum, a zero-dimensional test problem is developed. Using the test problem, it is demonstrated that the improper mixing of the fission spectrum results in an eigenvalue error of approximately 65 pcm. Though this may seem small, any error in such a simple calculation is unacceptable. In similar infinite-homogeneous test problems, eigenvalue errors of more than 300 pcm have been observed. It is also shown that the error due to fission spectrum mixing can be exaggerated for computer program verification purposes. It is concluded that the proper mixture of the neutron fission spectrum is essential for accurate neutron transport simulations. The effect of improper neutron fission spectrum mixture is demonstrated and quantified and this test problem may be used for verification purposes in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

2019 LLNL Nuclear Science and Security Summer Internship Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Science and Security Summer Internship Program (NS 3 IP) is designed to give graduate students an opportunity to come to LLNL for 8-10 weeks of hands-on research. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry), and gain a better understanding of the various science programs at LLNL.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗
DOE OSTI2021

Task Parallelism to Optimize Performance of Environmental Modeling Software

Climate modeling is an integral part of environmental research, from studying rare phenomena to predicting future climate trends. The need for more accurate models is only growing, but as climate modeling capabilities advance, existing workflows require optimization to recoup performance. A solution comes in the form of task parallelism, a novel programming capability that provides an opportunity for optimization at execution time by allowing tasks to be executed in parallel, reducing runtime significantly. Using Parsl, an intuitive and scalable parallel scripting library for Python, we implement task parallelism within support software to aid in the continuous advancement of climate modeling technology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

INTERSECT Architecture Specification: Use Case Design Patterns (V.0.5)

Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the federated microservice architecture specification. This document represents the science use case design pattern catalog of the INTERSECT Open Architecture Specification.

42 ENGINEERING↗