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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.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Systematic characterization of unknown compounds via dimensionality reduction of time series

Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Search for 𝑏-hadron decays to long-lived particles in the CMS endcap muon detectors

A search for long-lived particles originating from the decay of 𝑏 hadrons produced in proton-proton collisions with a center-of-mass energy of 13 TeV at the LHC is presented. The analysis is performed on a dataset recorded in 2018, corresponding to an integrated luminosity of 41.6 fb −1 . Interactions of the long-lived particles in the CMS endcap muon system would create hadronic or electromagnetic showers, producing clusters of detector hits. Selected events contain at least one such high-multiplicity cluster in the muon endcaps and require the presence of a displaced muon. The most stringent upper limits to date on the branching fraction ℬ⁡(𝐵 →𝐾⁢Φ), where the long-lived particle Φ decays to a pair of hadrons, are obtained for Φ masses of 0.3–3.0 GeV and Φ mean proper decay lengths in the range of 1–500 cm.

Hayrapetyan, A. [Yerevan Physics Institute]↗
DOE OSTI2025

Effect of calcite filler on carbonation behavior in a synthesized C‐S‐H binder

Widespread implementation of portland limestone cements (PLC) in industry has raised new questions about their carbonation resistance due to higher initial limestone content than ordinary portland cement. While carbonation of portland cement and each of its hydrate phases has been studied at a fundamental level, the integral role of how limestone fillers interact chemically or physically during carbonation necessitates further study. In this study, a C-S-H binder comprised of a highly reactive zeolite pozzolan and lime was used to evaluate the effect of varying calcite additions on carbonation behavior at a microstructural level. Fundamental insight into hydration kinetics and carbonation mechanisms of hydraulic binders was obtained via analysis of the C-S-H and calcite microstructure. X-ray microcomputed tomography (X-ray CT) was used to quantify microstructural changes, and the results showed a decrease in shrinkage for increased calcite dosages, with a ∼50% reduction observed for a 45% wt. calcite replacement level. Crystallographic and thermal analyses measured compositional changes in the binder and confirmed that shrinkage was related to the decalcification of C-S-H. Furthermore, the addition of calcite to the C-S-H binder effectively reduced the liquid-to-solid ratio, porosity, and amount of C-S-H present in these binders. Ultimately, these results help elucidate how the carbonation risks of PLC can be mitigated by proportioning the mixture water only to the reactive component of the binder.

C-S-H↗
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2024

Computational simulations and beamline optimizations for an electron beam degrader at CEBAF

An electron beam degrader is under development with the objective of measuring the transverse and longitudinal acceptance of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. This project is in support of the CE+BAF positron capability. Computational simulations of beam-target interactions and particle tracking were performed integrating the GEANT4 and Elegant toolkits. A solenoid was added to the setup to control the beam's divergence. Parameter optimization of the solenoid field and magnetic quadrupoles gradient was also performed to further reduce particle loss through the rest of the injector beamline.

Lizárraga-Rubio, V.↗
DOE OSTI2020

Brief Update on Advancements by Commercial Vendors/Options for Feeding and Pumping Biomass Slurries for Hydrothermal Liquefaction

In 2011, Pacific Northwest National Laboratory (PNNL), working as a partner in the National Advanced Biofuels Consortium, issued a report assessing the viable industrial feed system and pump options for a 2000 dry metric tons per day production-scale hydrothermal liquefaction (HTL) plant (Berglin et al. 2012). This report was issued as PNNL-21981, Review and Assessment of Commercial Vendors/Options for Feeding and Pumping Biomass Slurries for Hydrothermal Liquefaction, and will be referred to as “Pump Survey” for reference. It had the following scope: • Identify relevant applications and industries operating at comparable scales • Interact with commercial vendors and pursue integration between vendors • Take advantage of commercial off-the-shelf (COTS) technology • Supply and support vendor needs for biomass feedstock rheology characterization • Provide vendors with process requirements to support selection of conceptual design for feedstock handing and pump system • Obtain cost data to the extent possible from equipment vendors to support the technical economic analysis (TEA) • Identify issues associated scale-up. The current document represents an update to the prior report in order to: 1. Identify advancements in vendor-reported technologies 2. List reports of new experience or trials with biomass pumping applications 3. Determine if technology/application gaps identified in the prior report have been filled 4. Capture ideas on future research.

09 BIOMASS FUELS↗
DOE OSTI2022

Atomic Force Microscope Active Optical Probe for Single-Molecule Imaging and Time-Resolved Optical Spectroscopy (DOE SBIR Phase II/IIA Final Report)

Actoprobe LLC reports on the results of its DOE SBIR Phase II/IIA project on the development of Atomic Force Microscope Active Optical Probe for Single-Molecule Imaging and Time-Resolved Optical Spectroscopy. While chemistry science and technology greatly benefit from Atomic Force Microscopy in surface characterization, time-resolved chemical imaging on the single-molecule level lags far behind. Current scanning probe microscopy only obtains information about mechanical but not optical/chemical properties. To address this problem, the Actoprobe LLC research team has proposed a novel class of Atomic Force Microscopy probes, Ultra-Fast Pulsed Active Atomic Force Microscopy Optical Probes (UFP AAOPs), that will allow ultrafast time-resolved optical and chemical imaging at the nanoscale. As envisioned, these unique optical probes will perform the functions of conventional Atomic Force Microscopy probes and, in addition, will simultaneously provide chemical information about molecular scale interactions. This innovation is accomplished by integrating an ultrafast pulsed Quantum Dot laser source into an Atomic Force Microscopy probe. This report describes our progress with the fabrication of an ultrafast micrometer-size semiconductor laser, based on “artificial atoms” - Quantum Dots, integrated with an Atomic Force Microscopy probe. In this Phase II/IIA project, we have demonstrated the feasibility of the UFP AAOP concept by fabricating a first prototype of the UFP AAOP. The excellent performance of the probe has been proven in terms of AFM and optical spatial resolution through rigorous tests. The UFP AAOP provides pulses with less than 4 ps duration and higher than 11 GHz repetition rate, and spatial resolution better than 300 nm at 1240 nm wavelength. Technically, it is possible to reduce the pulse width to less than 1 ps and to improve lateral resolution to ~ 0.5 nm, which implies the potential capability for the probe to characterize chemical compounds with single-molecule resolution. The UFP AAOP fabrication procedure has been developed for wafer-scale production of multiple devices, with the yield of the process estimated to be lower than 1% with the limited fabrication capabilities and equipment available for use in the research project. However, using high-volume production tools and special GaAs processing equipment, the yield can be significantly improved, theoretically to ~ 50%. Finally, economic feasibility and scale-up manufacturing potential were analyzed for UFP AAOP and found to be very promising. In summary, the Actoprobe team has successfully demonstrated the feasibility of the UFP AAOP concept.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Research Priorities and Opportunities in U.S. Wholesale Electricity Markets: Market Design under Deep Decarbonization

This report provides a comprehensive review of challenges, research needs, and potential solutions for competitive wholesale electricity market design in deeply decarbonized power systems. We provide context regarding how competitive wholesale electricity markets can evolve in a longer-term perspective to ensure that they still operate efficiently throughout the transition to a deeply decarbonized future. We organize the discussion across seven topics: operational reliability, emerging technology integration, adequacy and resilience, price formation, interactions across transmission and distribution systems, transmission planning, achieving clean energy objectives, and challenges associated with cost-effectively achieving clean energy objectives. In each section we first identify key associated challenges before proposing a set of corresponding solutions and research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Bayesian Attack Model (BAM) User Story

This document presents a user story for the Bayesian Attack Model (BAM) tool designed to aggregate and analyze cyber-attack observables for operational technology (OT) systems. BAM aims to empower cybersecurity analysts by providing a streamlined interface for collecting observable data from various sources, enabling real-time analysis of potential adversary activity. By enhancing the response capabilities of security teams, BAM facilitates risk-informed decision-making and improves organizational security posture. This user story outlines the key functionalities, user interactions, and requirements necessary to successfully integrate BAM with other security information and event management (SIEM) technology and cybersecurity operations centers (CSOCs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Corrected Moments in Antenna Coordinates (CMAC) Technical Report

Various corrections are needed in order to extract the best value of the measurements from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility’s scanning precipitation radars. The Corrected Moments in Antenna Coordinates (CMAC) Value-Added Product provides an application chain for providing radar moments that are corrected for artifacts such as aliasing, attenuation, multi-trip echoes, and beam blockage. This technical report provides details on the entire process of correcting the ARM scanning precipitation radar data using CMAC. CMAC has been used at multiple ARM sites and ARM Mobile Facility deployments including the Tracking Aerosol Convection Interactions Experiment (TRACER), Cloud, Aerosol, and Complex Terrain Interactions (CACTI), and the Surface Atmosphere Integrated Laboratory (SAIL).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Grid Service Values of Generic Marginal Building Flexibility in Modeled 2030 U.S. Power Systems

The datasets include the capacity, energy, and ancillary service values of a marginal kilowatt-hour (kWh) of generic, daily, shiftable building flexibility as a presumed market entrant in the 2030 U.S. power systems. The results should be interpreted along with the caveats listed in "Valuation of Building Flexibility: Grid Service Value for Initial Market Entrants in Projected 2030 United States Power Systems", which also documents the methods used for the study. The factors examined in the study include: grid scenario, region, original usage hour in the day (local time), and building flexibility parameters - efficiency, dissipation, and shifting window include max pre-shift and max post-shift. Filenames in the datasets: The filenames contain information on the grid scenario, and the building flexibility efficiency and dissipation. For example: MidCase_2030_efficiency1.25_dissipation0.05_value.csv contains all results under Mid RE 2020, efficiency = 1.25, and dissipation = 0.05. * MidCase = Mid RE Each .csv file contains: region: The location of the building flexibility, aligned with U.S. Energy Information Administration (EIA) National Energy Modeling System (NEMS) Electricity Market Module regions. max_pre_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift earlier. max_post_shift: Part of building flexibility shifting window parameter, indicates the max number of hours the building flexibility can shift later. local_datetime: Original datetime of 1kWh of building energy consumption. local_orig_h: Original usage hour (1-24) of building energy consumption, ignores daylight saving. local_shift_to: The datetime to when building energy consumption shifts, if shifting happens. If no shifting occurs, this cell is left blank. energy: Net energy value of energy shifting. capacity: Net capacity value of energy shifting. shifting_value: Net energy plus capacity value of energy shifting. spin: Spin reserve value at the original datetime of consumption. flex: Flexible reserve value at the original datetime of consumption. reg: Regulation reserve value at the original datetime of consumption. total_profit: Assuming the building flexibility is capable of providing energy, capacity, and ancillary services, total profit is the maximum value of energy shifting value, spin reserve value, flexible reserve value, and regulation reserve value - one of the four choices at any given hour - because we do not allow its value to be double-counted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Sustainable Campus with PEV and Microgrid

Market penetration of electric vehicles (EVs) is gaining momentum, as is the move towards increasingly distributed, clean and renewable electricity sources. EV charging shifts a significant portion of transportation energy use onto building electricity meters. Hence, integration strategies for energy-efficiency in buildings and transport sectors are of increasing importance. This paper focuses on a portion of that integration: the analysis of an optimal interaction of EVs with a building-serving transformer, and coupling it to a microgrid that includes PV, a fuel cell and a natural gas micro-turbine. The test-case is the Nanyang Technological University (NTU), Singapore campus. The system under study is the Laboratory of Clean Energy Research (LaCER) Lab that houses the award winning Microgrid Energy Management System (MG-EMS) project. The paper analyses three different case scenarios to estimate the number of EVs that can be supported by the building transformer serving LaCER. An approximation of the actual load data collected for the building into different time intervals is performed for a transformer loss of life (LOL) calculation. The additional EV loads that can be supported by the transformer with and without the microgrid are analyzed. The numbers of possible EVs that can be charged at any given time under the three scenarios are also determined. The possibility of using EV fleet at NTU campus to achieve demand response capability and intermittent PV output leveling through vehicle to grid (V2G) technology and building energy management systems is also explored.

Singh, Reshma↗
DOE OSTI2025

Measurement of charged-current muon neutrino-argon interactions without pions in the final state using the MicroBooNE detector

We report a new measurement of flux-integrated differential cross sections for charged-current (CC) muon neutrino interactions with argon nuclei that produce no final-state pions (𝜈 𝜇 ⁢CC⁢0⁢𝜋). These interactions are of particular importance as a topologically defined signal dominated by quasielasticlike interactions. This measurement was performed with the MicroBooNE liquid argon time projection chamber detector located at the Fermilab Booster Neutrino Beam and uses an exposure of 1.3 ×10 21 protons on target collected between 2015 and 2020. The results are presented in terms of single- and double-differential cross sections as a function of the final-state muon momentum and angle. The data are compared with widely used neutrino event generators. We find good agreement with the single-differential measurements, while only a subset of generators are also able to adequately describe the data in double-differential distributions. This work facilitates comparison with Cherenkov detector measurements, including those located at the Booster Neutrino Be

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Manufactured solutions for an electromagnetic slot model

The accurate modeling of electromagnetic penetration is an important topic in computational electromagnetics. Electromagnetic penetration occurs through intentional or inadvertent openings in an otherwise closed electromagnetic scatterer, which prevent the contents from being fully shielded from external fields. To efficiently model electromagnetic penetration, aperture or slot models can be used with surface integral equations to solve Maxwell's equations. A necessary step towards establishing the credibility of these models is to assess the correctness of the implementation of the underlying numerical methods through code verification. Surface integral equations and slot models yield multiple interacting sources of numerical error and other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the electric-field integral equation with a slot model. Finally, we demonstrate the effectiveness of these approaches for a variety of cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗