Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “spatial optimization”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2023

Justice 40 Tool (J40 Tool) v1.0

The Justice 40 tool provides a quantitative framework to support decision-making around equitable energy interventions at the community level. The tool calculates the optimal portfolio of policy interventions that explicitly mitigates energy insecurity of an eligible population, by reducing its disproportionate energy burden. The place-based analysis assumes a spatial census tract-level resolution and distinguishes different sociodemographic groups within each tract. Instead of focusing on a specific technology, the underlying J40 model captures the combined effect of a set of policy interventions, currently including weatherization, rooftop solar, community solar and community wind. For each tract, the model chooses the optimal (least cost) combination of interventions to address the disproportionate burden, considering the specific population demographics and techno-economic potentials of technologies. Mathematically, this problem is formalized as an optimization model and formulated as a linear program.

Heleno, Miguel↗
DOE OSTI2020

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗
DOE OSTI2020

Multi-Functional Distributed Fiber Sensors for Pipeline Monitoring and Methane Detections. Final Report

As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.

03 NATURAL GAS↗
DOE OSTI2020

Assessment of the feedstock supply for siting single- and multiple-feedstock biorefineries in the USA and identification of prevalent feedstocks

An integrated multi-feedstock bioenergy (i.e., biofuel, biopower, or bioproduct) supply system has potential to reduce biomass supply system uncertainties and costs. This study identifies optimal configurations of multi-feedstock biomass-to-biorefinery supply chains and pertinent feedstock combinations based on spatial distribution of feedstock and lowest delivered cost to the biorefinery. We used the Supply Characterization Model (SCM) to allocate feedstock supplies to candidate biorefinery facilities. Model runs were performed for herbaceous energy crops, agriculture residue, and woody biomass available in 2017, 2022, 2025, and 2030 as estimated by the Policy Analysis System (POLYSYS) and Forest Sustainable and Economic Analysis Model (ForSEAM) models. Three feedstock supply scenarios were compared: (a) an herbaceous scenario: switchgrass, miscanthus, biosorghum, and corn stover; (b) a woody scenario: coppice wood, noncoppice wood, whole trees, and forestry residues, and (c) a mixed scenario: a combination of all feedstocks in herbaceous and woody scenarios. By 2030 the analyses predicted that 323, 168, and 473 biorefineries were sited in the herbaceous, woody, and mixed scenario, respectively, in the conterminous USA. Feedstock mixes supplied to the biorefineries were mostly dominated by a single feedstock. The most prominent feedstock mixes identified were: (1) switchgrass and miscanthus; (2) coppice and noncoppice wood; and (3) coppice wood, noncoppice wood, switchgrass and miscanthus. Biorefineries using multi-feedstock would be beneficial for growth of bioeconomy, however flexible and cost-effective conversion platforms should be developed to efficiently utilize multiple feedstocks. This analysis identifies biorefinery locations and feedstock supply mixes while minimizing delivered feedstock costs based on spatial and temporal feedstock availability.

09 BIOMASS FUELS↗
DOE OSTI2021

High spatial resolution temperature profile measurements of solid-oxide fuel cells

Temperature gradients resulting from local electrochemical reactions, current distribution and geometry of gas flow channels in solid oxide fuel cells (SOFCs) create thermal stresses, localized thermophysical property gradients and uneven property evolution, contributing to SOFC degradation. This paper presents a new method to perform temperature measurements (up to 800°C) at high spatial resolutions to monitor the operation of SOFCs. Using femtosecond laser irradiation, distributed fiber sensors were hardened for high temperature environment applications. Distributed fiber sensors were embedded in interconnected plates using an additive manufacturing method to perform temperature measurements with 4-mm spatial resolution during the operation of a planar fuel cell. The measurement revealed the impact of various H 2 fuel concentrations and current loads have on temperature profiles of the SOFC tested. Temperature variation on the anode side was found to be less than 5°C, and 3°C on the cathode side. The measurements were compared to results from a multiphysics fuel cell performance model simulating similar conditions. These simulations predicted similar temperature gradients, indicating the experimental data obtained is reasonable. The model also predicts that the effect of the embedded sensor has on the local temperature will be minimal and that the gradient of temperature in the gas channels will be captured despite the separation between the sensor and the gas flow. Finally, the high spatial resolution data harnessed by these distributed fiber sensors provides experimental support for model-based design and optimization to improve the operational efficiency and longevity of solid oxide fuel cells and fuel cell assemblies.

25 ENERGY STORAGE↗
DOE OSTI2022

Three-dimensional Skyrme Hartree-Fock-Bogoliubov solver in coordinate-space representation

The coordinate-space representation of the Hartree-Fock-Bogoliubov theory is the method of choice to study weakly bound nuclei whose properties are affected by the quasiparticle continuum space. To describe such systems, we developed a three-dimensional Skyrme-Hartree-Fock-Bogoliubov solver HFBFFT based on the existing, highly optimized and parallelized Skyrme-Hartree-Fock code Sky3D. The code does not impose any self-consistent spatial symmetries such as mirror inversions or parity. The underlying equations are solved in HFBFFT directly in the canonical basis using the fast Fourier transform. To remedy the problems with pairing collapse, we implemented the soft energy cutoff and pairing annealing. The convergence of HFB solutions was improved by a sub-iteration method. The Hermiticity violation of differential operators brought by Fourier-transform-based differentiation has also been solved. Furthermore, the accuracy and performance of HFBFFT were tested by benchmarking it against other HFB codes, both spherical and deformed, for a set of nuclei, both well-bound and weakly-bound.

3D coordinate-space representation↗
DOE OSTI2021

Insight into SEI Growth in Li-Ion Batteries using Molecular Dynamics and Accelerated Chemical Reactions

The performance of lithium-ion batteries (LIB) using organic electrolytes strongly depends on the formation of a stable solid electrolyte interphase (SEI) film. Elucidating the dynamic evolution and spatial composition of the SEI can be very useful to study the stability of SEI components and help optimize the formation cycles of LIB. We propose a classical molecular dynamics simulation protocol for predicting the first stages of SEI formation using a reaction method involving the decomposition of EC and LiPF 6 molecules in the electrolyte. We accelerate the formation of SEI components near the anode surface by increasing the probability of reactions, implemented through a geometry matching scheme, followed by a force-field reconfiguration. We observe the formation of gases (C 2 H 4 ), inorganic (Li 2 CO 3 and LiF) and organic (LEDC) components. Finally, this protocol shows promise to be able to evaluate the effects of varying electrolyte compositions and additives on SEI layer structure and composition.

25 ENERGY STORAGE↗
DOE OSTI2022

Inverse Calculation of Burden Distribution Matrix Using B-spline Model Based PDF control in Blast Furnace Burden Charging Process

The inverse calculation of burden distribution matrix (BDM) is one of the most important challenges in the blast furnace operation in iron-making processes. In general, blast furnace consumes 65% of the total energy for the whole steel-making. Focusing on this practical challenge, this article proposes a new burden distribution spatial model in calculating burden charging process, and develops a B-spline approximation-based probability density function (PDF) control algorithm to assign the expected thickness distribution of burden layer and, thus, develops a new method for the required inverse calculation of BDM. First, a novel method for the thickness distribution of burden layer is given using B-spline model to produce an expected distribution shape subjected to a desired tracking within a specific spatial constraint. Then, according to the coexistence of continuous and bounded discrete variables in BDM, a novel hybrid optimization control method by combining integer programming and PDF tracking is further established for the effective inverse calculation of BDM. Finally, the proposed PDF-based iterative inverse calculation of BDM using B-spline models are tested using various data from industrial examples. Furthermore, the simulation results show that the proposed method is well suited to solve the BDM inverse calculation problem in practice.

42 ENGINEERING↗
DOE OSTI2021

BEAM COLLIMATION IN THE PIP-II LINAC TO BOOSTER TRANSFER LINE

The new PIP-II superconducting linac will deliver a 2 mA average H⁻ beam to the existing Booster synchrotron. The injected beam is accumulated by charge exchange over approximately 300 turns; phase space painting is used to mitigate space charge effects. To limit the power load on the internal waste beam absorber from the transverse tails of the H⁻ distribution missing the foil, the beam will be collimated in both planes in the linac to Booster transfer line using compact collimators of a novel design. Both the number of parasitic hits and the fraction of the beam missing the foil are sensitive functions of the H⁻ beam centroid position with respect to the edge of the foil. The positioning of the collimation is constrained by the availability of suitable space in the transfer line lattice, by specifics of the collimator design, by the phase space orientation at the collimator, and by the betatron phase advance to the foil needed to achieve proper orientation of the spatial distribution at the injection point. In this contribution, we describe the procedure by which collimator positions were optimized. We then discuss the expected performance of the overall system.

Johnson, David E.↗
DOE OSTI2024

Generating Euler Diagrams Through Combinatorial Optimization

Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.

Computer Science↗
DOE OSTI2025

Data-Driven Method for Groundwater-Level Mapping and Monitoring-Well Network Optimization at Hanford

This report summarizes the initial results and outcomes of a physics-informed, data-driven groundwater level (GWL) mapping capability for the Hanford Site. GWL mapping at Hanford is typically conducted annually and requires a significant amount of computational and expert resources, and it does not allow assessment of the informational value of specific monitoring wells. The proposed method produces spatially and temporally resolved fields consistent with sparse, irregularly sampled, and nonuniformly distributed well measurements. Implemented successfully, this capability will allow rapid mapping of groundwater levels and provide an opportunity to optimize monitoring activities (both location and sampling frequency) based on data information value evaluation. The approach integrates a diffusion-based generative model – trained on MODFLOW simulation data from the Plateau-to-River (P2R) model – with score-based data assimilation (SDA), allowing observation-conditioned mapping without retraining for each monitoring-network layout.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

42 ENGINEERING↗
DOE OSTI2021

Reduced ecosystem resilience quantifies fine-scale heterogeneity in tropical forest mortality responses to drought

Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, in this study, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one- out cross-validation R 2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to “fingerprint” forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques

Brillouin Optical Time Domain Analysis (BOTDA) sensor systems play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond tens of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Optimizing Raman pump and probe wave profiles ensures the robustness of Brillouin scattering signals, effectively countering attenuation-induced losses. Additionally, enhancing weak Brillouin signals by strategically placing EDFAs at optimal fiber locations keeps sensor sensitivity high. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. In summary, this research has shown a holistic exploration of extending BOTDA’s distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.

Bhatta, Hari↗
DOE OSTI2020

Improved Performance of Cu(InGa)(SeS) 2 PV Modules Using the Reaction of Metal Precursors. Final Report

This project “Improved Performance of Cu(InGa)(SeS) 2 PV Modules using the Reaction of Metal Precursors” was a partnership led by the Institute of Energy Conversion (IEC) at the University of Delaware with Columbia University and the Molecular Foundry at the Lawrence Berkeley National Laboratory. The aim was to develop pathways to improve Cu(InGa)(SeS) 2 (CIGSS) thin film photovoltaic modules using processes compatible with low manufacturing cost. The CIGSS approach investigated was a two-step process including deposition of metal precursor films following by reaction in hydride gases utilizing IEC’s novel reactor. The process was similar to that under commercial development by the project’s industry partner Stion. When Stion went out of business mid-project the focus changed to a rapid thermal process considered more commercially viable. Approaches to improve the performance of solar cells using the reacted films focused on two material innovations. First, the overall Ga content was increased to increase the operating voltage, which is desirable for scale-up to commercial modules. Second, the processing and performance advantages arising from Ag alloying were investigated. Advanced characterization guided process and material development including control of relative composition gradients. Research on the formation of Cu-Ga-In metal precursors utilized sputtering deposition which is normally used in commercial applications. The work resulted in processes for deposition of precursor stacks with increased relative Ga content and effects of deposition parameters on morphology and phase composition were established. It was shown that the metal precursor films have comparable phase composition and morphology so subsequent reaction follows from the same starting point. The addition of Ag to the metal precursors gave more uniform morphology and improved adhesion of reacted films which enable higher reaction temperature for faster processing. A novel outcome was the discovery of a previously undocumented material phase in sputter-deposited and evaporated Ag-Cu-In-Ga thin films. Hydride gas reaction processes including time-temperature-concentration profiles were developed for different precursor compositions. This enables control of composition profiles to engineer through-film gradients for solar cell optimization with characterization and simulations used to correlate measured film composition profiles to measurements of devices. In particular, the gradient of sulfur at the front of the CIGSS film was found to be critical. The simulations guided process development leading to improved reproducibility of devices improved performance with higher Ga content and higher voltage. With Ag-alloyed precursors, the reaction pathways leading were determined. A significant finding was that Ag-alloying increases the reaction rate to completely convert precursor films to the final chalcopyrite which could enable reduced reaction time to benefit manufacturability. To maintain potential commercial viability, the process under investigation was refocused to a rapid thermal process that could potentially be incorporated into an in-line process for manufacturing. Precursors with different composition were capped with an extra selenium layer and reacted in hydrogen sulfide 5-15 minutes, compared to typically 2 hours in the previous multi-step batch process. Critical RTP parameters were identified to control the reaction. Further optimization would be needed for high efficiency solar cells but pathways to high quality devices with further optimization and improved heating uniformity were developed. The project also developed new optoelectronic characterization approaches with a focus on development and application of spatial- and time-resolved photoluminescence and a custom mapping photoluminescence microscope built. It was shown how critical electronic transport properties strongly depend on the chemical composition of the material and that a wide range of samples show inhomogeneity on a length scale larger than the grains in the films. Additionally, two-photon excitation capability was developed to distinguish bulk vs surface losses. The project advances the state-of-the -art for precursor reaction processes in several ways that could impact manufacturing. This includes validation of approaches to increase voltage and establishment of model-guided control to form optimal composition profiles. The application of process control approaches with knowledge of phase formation and reaction pathways can be critically valuable in designing a large-scale process.

14 SOLAR ENERGY↗
DOE OSTI2020

An analysis of the spatio-temporal resolution of the immersed boundary method with direct forcing

The immersed boundary method (IBM) with direct forcing is very popular in the simulation of rigid particulate flows. In the IBM, an interaction force is introduced at the interface between fluid and particle in order to approximate the no-slip boundary condition. The interaction force is calculated through dividing the velocity difference (or error) between fluid and particle at the interface by the time step. Here, a dynamic equation for the velocity difference is derived. Additionally, analyses on the dynamic equation provide a few new findings: (i) The interaction force is the solution of a least-squares error problem, with the direct implication that the Lagrangian marker distribution has no effect on the large scale flow structure once the distribution of Lagrangian markers become saturated along the interface (i.e., each marker remains properly correlated with all its neighbors); (ii) The Lagrangian volume-weight is a relaxation factor to control how fast the velocity error decays to the ideal value of zero; (iii) The optimal choice of the Lagrangian volume-weight is the largest value permissible by a stability condition. A comprehensive convergence analysis with regard to the spatial and temporal resolution is presented for the velocity error and also for the shear-stress and surface pressure. In three simple canonical problems, it is analytically and numerically shown that the IBM results converge to the theoretical solutions obtained with precise imposition of no-slip and no-penetration boundary conditions. It is observed that it is not necessary to match the Lagrangian marker volume-weight to that of the local Eulerian cell volume and in fact this matching leads to lower than optimal computational efficiency. However, it is found that extremely high Eulerian grid resolution and small time step have to be used to obtain high precision simulation results. Especially, the time step should be inversely proportional to the particle Reynolds number for low Reynolds number flows. For high frequency oscillation problems, the grid size needs to be reduced by a factor of the square root of the frequency, and the time step to be reduced by a factor of the frequency. The theoretical findings here can be used to alleviate the technical difficulties in simulating non-spherical particles by not requiring the Lagrangian marker distribution to match the Eulerian grids and also in the implementation of IBM on non-uniform Eulerian grids. The present work also provides simple practical guidance on the choice of temporal and spatial resolution so as to control the simulation error a priori.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Observing Arctic Sea Ice

Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend observational-scale issues. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward coordinating across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system.

54 ENVIRONMENTAL SCIENCES↗