Search NASA⌕ Search

SEARCH · Search NASA

Results for “Variable selection”

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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗
DOE OSTI2018

Development of Hot-Isostatic Pressing for Treatment of UK ILW Radioactive Wastes - 18280

Hot-Isostatic Pressing (HIPing) has been proposed as an improved method for the treatment of radioactive wastes in the UK. Sludges and slurries housed on the Sellafield site are a prime candidate for HIP treatment due to their large volumes and issues associated with the use of the baseline cementation strategy. Options have been developed by GeoRoc Ltd to co-process these wastes, including a detailed conceptual plant design. This paper will describe research and development aimed at demonstrating the viability of the concept presented in this design. This paper summarizes two studies performed to address the knowledge gaps identified for treatment of these wastes using HIP technology. The first study related to the poorly characterized nature of the wastes, which requires any treatment option to be flexible in terms of the chemical and physical properties of the waste feed. The second study involved demonstrating that HIPing can be effectively utilized at a scale applicable to treat a waste inventory estimated to be in excess of 5,000 m{sup 3}. The first section of this paper presents a summary of research carried out to determine where variation in the waste stream composition may affect the processing and wasteform produced when HIPing Magnox sludges. This study aimed to qualify where restrictions on the process envelope may exist due to poorly defined waste-stream characteristics. A baseline wasteform was selected with the aim of achieving significant waste volume reduction, while producing a physically and chemically stable wasteform. A glass-bonded magnesium-titanate/magnesium-silicate wasteform was selected as the preferred option for the Magnox sludge wastes. A series of studies investigated the densification and phase formation of waste-forms by altering key variables in the Magnox sludge simulant. To test the extremes of the processable waste loading, samples were produced and characterized at 100 wt% waste-loading for Magnox sludge, Site-Ion Exchange Plant (SIXEP) sand/clinoptilolite and a 50:50 mix of the two waste-streams. Further studies involved a) altering the waste loading to additive mass ratio (to simulate changes in the waste loading/sludge solids content), b) varying the particle size distribution from the datum to include only small or large particulates in the sludge feed; and c) introducing up to 20 wt% metallic Magnox alloy, organic species or carbonate materials (to determine how their inclusion may affect the processing and densification of the materials). A U{sub 3}O{sub 8} loaded sample was also produced using the baseline Magnox sludge formulation. All the tests performed were shown to produce monolithic, well densified waste-forms with substantial volume reductions. Minimal variations from the baseline were noted but the waste-forms produced with these deviations was determined to be of comparable quality to of the baseline product. U{sub 3}O{sub 8} was shown to behave in a similar fashion to the ZrO{sub 2} surrogate used to simulate it in the process envelope studies and was encapsulated in the magnesium titanate/magnesium silicate matrix. This work demonstrates that HIP technology can successfully produce waste-forms across the broad process envelope expected in the Magnox sludge inventory. It is also possible to produce monolithic waste-forms at 100 wt% waste loading from Magnox sludges, SIXEP sand clinoptilolite slurries or a co-processed mixture of the two. The second part of this paper summarizes a commercially relevant demonstration of HIP technology for the treatment of sludge and slurry wastes housed at the Sellafield site (UK). This study culminated in production of the largest HIPed wasteform successfully produced to date. This study was performed in a series of stages. Investigations into formulation and properties of waste-forms were performed at a lab scale to evaluate suitable wasteform systems and identify suitable combinations of additives and waste loadings. Scale up demonstrations were then performed in stages with custom-designed HIP canisters. Scale-up and characterization of key wasteform properties were successfully performed with initial canister volumes of 50 ml, 400 ml and 8 L. Suitable front-end unit operations to produce a granular, free-flowing calcined simulant were demonstrated at an engineering scale. The demonstration was successful in processing a HIP canister with an initial starting volume in excess of 100 L. A 75 % volume reduction from the raw sludge was achieved at scale while demonstrating that HIP is applicable at suitably large scale for immobilization of ILW waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Oxaliplatin and irinotecan induce heterogenous changes in the EMT markers of metastasizing colorectal carcinoma cells

Highlights: • Significant variability and lower expression uniformity in lymph node metastatic cells. • Heterogeneity of cell response partially determined by cancer cell microenvironment. • Metastatic cells proved to be less sensitive to chemotherapy treatment compared to primary site cancer cells. • Irinotecan and oxaliplatin affected EMT markers in primary cell lines. In patients with advanced colorectal cancer (CRC), surgery is complemented with systemic therapy – chemotherapy and radiochemotherapy. Although the patients’ overall survival has been significantly improved, tumor resistance is still a frequent cause of chemotherapy failure. Several factors contribute to chemoresistance of tumor cells including changes related with epithelial-mesenchymal transition (EMT). The present study was designed to verify the presence of EMT markers in paired CRC primary cell lines obtained from primary tumor sites and lymph node metastases of three patients and to investigate the effect of irinotecan and oxaliplatin treatment on these markers as well. The samples of the higher stage of CRC and positive for angioinvasion were selected and qPCR, western blot analysis, migration assay, cytotoxicity testing was performed. Results confirmed the increased expression of several markers characteristic of EMT and invasiveness in lymph node metastatic cells, with a significant variability between individual samples. Irinotecan and oxaliplatin decreased migration activity of the cells and to the varying degree affected the expression of EMT and invasiveness markers. In conclusion, in CRC EMT is present in metastatic cells over a phenotypic continuum whose expression is altered heterogeneously upon irinotecan and oxaliplatin treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The need for ion-exchange membranes with high charge densities

Ion-exchange membranes (IEMs) are essential for controlling ion transport in electrochemical membrane-based technologies for water purification, energy generation, and energy storage. In general, IEMs must exhibit high ionic conductivity and selectivity, both of which are significantly influenced by the charge and water content of the hydrated membrane. In this perspective, we compiled the charge and water contents of approximately 1,000 IEMs reported over the past several decades to assess their range and variability, as well as to reveal gaps and set clear targets for future membrane design. We compared the charge and water content of negatively and positively charged membranes, as well as membranes developed for desalination and energy applications. The majority of IEMs, regardless of structure or intended application, have volumetric charge densities below 3 mol/L[membrane]. This narrow range of charge densities limits the performance of IEMs and contributes to the observed tradeoff between ion throughput and selectivity. It is critical to develop IEMs with higher charge densities to overcome this performance limitation. Further, we highlighted several design strategies that yielded IEMs with fixed charge densities higher than 3 mol/L[membrane]. These strategies provide valuable insights and can guide the design of next-generation IEMs with high charge densities.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Nomograms for Prediction of Outcome With or Without Adjuvant Radiation Therapy for Patients With Endometrial Cancer: A Pooled Analysis of PORTEC-1 and PORTEC-2 Trials

Background: Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials: Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results: Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions: The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Characterization of Covetic Materials for Process Control. Final CRADA report

GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2019

Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM

In this paper, we study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4,000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear solution of classical three-wave interaction via finite-dimensional quantum model

The quantum three-wave interaction, the lowest-order nonlinear interaction in plasma physics, describes energy–momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum–classical correspondence far from the fixed point dynamics. We discuss the Poincaré recurrence of the system and the mitigation of quantum scrambling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Strain selection, biomass to biofuel conversion, and resource colocation have strong impacts on the economic performance of algae cultivation sites

Decisions involving strain selection, biomass to biofuel technology, and the location of cultivation facilities can strongly influence the economic viability of an algae-based biofuel enterprise. In this contribution we summarize our past results in a new analysis to explore the relative economic impact of these design choices. We present strain-specific growth model results from two saline strains (Nannocloropsis salina, Arthrospira sp.), a fresh to brackish strain (Chlorella sp., DOE strain 1412), and a freshwater strain of the order Sphaeropleales. Biomass to biofuel conversion is compared between lipid extraction (LE) and hydrothermal liquefaction (HTL) technologies. National-scale models of water, CO2 (as flue gas), land acquisition, site leveling, construction of connecting roads, and transport of HTL oil to existing refineries are used in conjunction with estimates of fuel value (from HTL) to prioritize and select from 88,692 unit farms (UF, 405 ha in pond area), a number sufficient to produce 136E+9 L yr-1 of renewable diesel (36 billion gallons yr-1, BGY). Strain selection and choice of conversion technology have large economic impacts, with differences between combinations of strains and biomass to biofuel technologies being up to $10 million dollars yr-1 UF-1. Results based on the most productive species, HTL-based fuel conversion, and resource costs show that the economic potential between geographic locations within the selection can differ by up to $4 million yr-1 UF-1, with 2.0 BGY of production possible from the most cost-effective sites. The local spatial variability in site rank is extreme, with very high and low rank sites within 10s of km of each other. Colocation with flue gas sources has a strong influence on site rank, but the most costly resource component varies from site to site. The highest rank sites are located predominantly in Florida and Texas, but most states south of 37°N latitude contain promising locations. Keywords: algae, biofuels, resource assessment, geographic information systems, techno-economics

algae, biofuels, resource assessment, geographic i↗
DOE OSTI2017

Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Is Knowledge About Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

feature selection↗
DOE OSTI2016

Characterization of U(VI)-Bearing Precipitates Produced by Ammonia Gas Injection Technology for Unsaturated Sediments - 16600

The processing of irradiated fuel to extract plutonium for the nuclear arsenal has resulted in a legacy of uranium contamination in the vadose zone (VZ) of the Hanford 200 Area. This contamination is a potential source for groundwater contamination, posing a risk to receptors through water uptake from contaminated wells or discharges to surface water. The injection of reactive gases is an innovative technology being evaluated for reducing the mobility of radionuclide contaminants in the Hanford vadose zone. Ammonia (NH{sub 3}) gas in particular has been used to reduce uranium mobility in laboratory scale studies and is being evaluated for field scale application. The sediment of the Hanford vadose zone is composed of a complex of mineral phases including quartz, feldspar, mica, and calcium carbonates. The silica content in the 200 Area subsurface markedly outweighs the majority of elements, with aluminum following closely. The injection of ammonia gas into the vadose zone is associated with an increase in pH conditions as the gas partitions into the pore water. The resulting alkaline conditions significantly increase the solubility of some aluminum and silicate-containing minerals within the system. As pH decreases to natural conditions, the aluminosilicate minerals will precipitate, potentially coating and sequestering the uranium contaminants. Although previous laboratory scale tests have shown decreased uranium mobility, there are many questions regarding the application of the remediation method to 200 Area vadose zone conditions. In particular, additional testing is required to understand the impact of various environmental factors on the formation of uranium mineral phases with the application of the NH{sub 3} injection method. In order to answer some of the unknowns regarding this remediation technology for the 200 Area vadose zone, an effort was made to characterize the uranium-bearing precipitates being formed with the injection of ammonia gas. Rather than a sediment-water system, a synthetic pore water solution was prepared using the ion concentrations relevant to the 200 Area vadose zone. These solutions were limited to select constituents believed to play a major role in the remediation process. The use of this carefully composed solution allows for the thorough evaluation of the contribution the variable components make to the formation of the solid analyte. The characterization of solid samples was attempted using a host of complementary analytical techniques including scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and X-ray diffraction (XRD). Though characterization of the samples is ongoing, the data produced thus far has allowed for the imaging and X-ray spectroscopy of the solid uranium phases produced as well as a tentative identification of cejkaite (Na{sub 4}(UO{sub 2})(CO{sub 3}){sub 3}) as the major uranium mineral phase. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

SU-F-E-20: A Mathematical Model of Linac Jaw Calibration Integrated with Collimator Walkout

Purpose: Accurate jaw calibration is possible, but it does not necessarily achieve good junctions because of collimator rotation walkout. We developed a mathematical model seeking to pick an origin for calibration that minimizes the collimator walkout effect. Methods: We use radioopaque markers aligned with crosshair on the EPID to determine the collimator walkout at collimator angles 0°, 90° and 270°. We can accurately calibrate jaws to any arbitrary origin near the radiation field centre. While the absolute position of an origin moves with the collimator walkout, its relative location to the crosshair is an invariant. We studied two approaches to select an optimal origin. One approach seeks to bring all three origin locations (0°–90°–270°) as close as possible by minimizing the perimeter of the triangle formed by these points. The other approach focuses on the gap for 0°–90° junctions. Results: Our perimeter cost function has two variables and non-linear behaviour. Generally, it does not have zero-perimeter-length solution which leads to perfect jaw matches. The zero solution can only be achieved, if the collimator rotates about a single fixed axis. In the second approach, we can always get perfect 0°–0° and 0°–90° junctions, because we ignore the 0°–270° situation. For our TrueBeams, both techniques for selecting an origin improved junction dose inhomogeneities to less than ±6%. Conclusion: Our model considers the general jaw matching with collimator rotations and proposes two potential solutions. One solution optimizes the junction gaps by considering all three collimator angles while the other only considers 0°–90°. The first solution will not give perfect matching, but can be clinically acceptable with minimized collimator walkout effect, while the second can have perfect junctions at the expense of the 0°–270° junctions. Different clinics might choose between these two methods basing on their clinical practices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems

Nudging techniques are commonly employed to constrain atmospheric simulations toward observed states, facilitating model evaluation and sensitivity studies. However, if applied improperly – particularly to thermodynamic variables such as temperature and humidity – nudging can distort physical processes and introduce spurious biases, undermining the credibility of the simulations. This study presents an improved nudging implementation that applies vertically modulated tendencies to reduce adverse impacts on model physics. The framework is tested in version 2 of the Energy Exascale Earth System Model (EAMv2) using a suite of hindcast simulations nudged toward ERA5 reanalysis. We systematically evaluate the individual and combined effects of nudging wind, temperature, and humidity fields on the model's ability to represent large-scale atmospheric states and high-impact weather systems. Results show that the revised strategy – particularly when nudging temperature and humidity at selected levels – enhances hindcast skill by improving agreement with ERA5 without degrading the hydrological cycle or precipitation processes. Additional improvements in surface temperature, outgoing longwave radiation, and precipitation biases are achieved through targeted nudging of land-surface variables. The proposed approach strengthens the representation of large-scale conditions relevant to tropical cyclones, atmospheric rivers, and extratropical cyclones in the low-resolution EAMv2. These findings demonstrate that carefully designed thermodynamic nudging, especially of temperature and humidity, improves the realism of constrained simulations and broadens the utility of nudged EAMv2 for atmospheric modeling, machine learning, and high-impact weather research.

Atmospheric river↗
DOE OSTI2016

Electrical capacitance as a predictor of root dry weight in shrub willow ( Salix ; Salicaceae) parents and progeny

Premise of the study: Root biomass is an important trait often disregarded in woody perennial selection due to the challenge and expense of accurately and efficiently measuring large populations. In this study, we aim to develop a simple method that can predict root dry weight within a diverse shrub willow ( Salix ) breeding population representing species hybrids and their parents using root electrical capacitance (REC). Methods: The REC method was tested on plants started from cuttings and grown in pots with potting mix in the greenhouse for 11 wk to assess the relationship of REC with 24 biomass traits and its usefulness in allometric models for root and stem dry biomass. Results: Strong linear and positive correlations were found between REC and root dry biomass ( r = 0.88). The total proportion of variance of root and stem dry biomass explained by predictors in multiple regression was 85% and 69%, respectively. The relative importance of predictor variables in allometric models was dominated by the contribution of REC. Discussion: This work provides an efficient and nondestructive technique to indirectly quantify root biomass of genetically diverse shrub willow progeny, which has great promise for selection of genotypes with varying root biomass and for the accurate estimation of belowground carbon sequestration.

59 BASIC BIOLOGICAL SCIENCES↗