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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advanced Method Optimization for Sampling and Analysis Instrumentation

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. In conclusion, this strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical technique.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress

As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation. We show that coral response to heat stress is a complex trait derived from multiple interactions among holobiont members. We identify host and photosymbiont genes that exhibit lineage-specific expression level adaptation and uncover potential roles for bacterial associates in supplementing the metabolic needs of the coral-photosymbiont duo during heat stress. Our results stress the importance of integrative and comparative approaches across a wide range of species to better understand coral survival under the predicted rise in sea surface temperatures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗
DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗
DOE OSTI2023

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia. We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Solar Cell Metallization Wear is Sensitive to Loading Frequency

Here, we performed cyclic loading of photovoltaic laminates with precracked silicon cells to explore if and how loading frequency and contact pressure influence the ensuing gridline wear-out process. A measurement of parallel resistance across cracked gridlines on a laminated cell coupon was used as the metric for gridline electrical contact degradation. A statistical analysis of variance (ANOVA) analysis of the experimental results discerned that loading frequency is a more significant factor than contact pressure for gridline degradation.

14 SOLAR ENERGY↗
DOE OSTI2023

New insights into the natural history of bronchopulmonary dysplasia from proteomics and multiplexed immunohistochemistry

Bronchopulmonary dysplasia (BPD) is a disease of prematurity related to the arrest of normal lung development. The objective of this study was to better understand how proteome modulation and cell-type shifts are noted in BPD pathology. Pediatric human donors aged 1–3 yr were classified based on history of prematurity and histopathology consistent with “healed” BPD (hBPD, n = 3) and “established” BPD (eBPD, n = 3) compared with respective full-term born (n = 6) age-matched term controls. Proteins were quantified by tandem mass spectroscopy with selected Western blot validations. Multiplexed immunofluorescence (MxIF) microscopy was performed on lung sections to enumerate cell types. Protein abundances and MxIF cell frequencies were compared among groups using ANOVA. Cell type and ontology enrichment were performed using an in-house tool and/or EnrichR. Proteomics detected 5,746 unique proteins, 186 upregulated and 534 downregulated, in eBPD versus control with fewer proteins differentially abundant in hBPD as compared with age-matched term controls. Cell-type enrichment suggested a loss of alveolar type I, alveolar type II, endothelial/capillary, and lymphatics, and an increase in smooth muscle and fibroblasts consistent with MxIF. Histochemistry and Western analysis also supported predictions of upregulated ferroptosis in eBPD versus control. Finally, several extracellular matrix components mapping to angiogenesis signaling pathways were altered in eBPD. Despite clear parsing by protein abundance, comparative MxIF analysis confirms phenotypic variability in BPD. This work provides the first demonstration of tandem mass spectrometry and multiplexed molecular analysis of human lung tissue for critical elucidation of BPD trajectory-defining factors into early childhood.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Geospatial analysis of preterm and small-for-gestational age births in Washington D.C.

Background: This study is based on the recognition that adverse pregnancy outcomes significantly affect maternal and infant health, leading to increased morbidity and mortality. These outcomes are shaped by a complex interplay of individual-level factors—like maternal age and education—and community-level influences, including socio-economic status and access to healthcare. Understanding these determinants is crucial for developing effective public health strategies, especially for marginalized populations, by identifying high-risk areas and informing targeted interventions that address both individual and structural barriers. Methods: We utilized geospatial analysis to explore the association between individual- and community-level factors and adverse pregnancy outcomes, specifically preterm birth (PTB) and small-for-gestational-age (SGA) birthweight in Washington, D.C. We used Empirical Bayes smoothing methods to calculate rates of adverse birth outcomes from 2010 to 2018 at the U.S. Census tract–level. Spatial scan statistics were used to investigate if adverse birth outcomes clustered in specific areas. ANOVA tests were conducted for individual- and community-level factors within identified clusters. Results: Spatial analysis identified significant high-risk clusters for PTB and SGA infants primarily in southeastern Washington, D.C., particularly in Wards 7 and 8. Individuals residing within these clusters experienced a 47% increased risk of PTB (RR = 1.467) and a 56% increased risk of SGA (RR = 1.560) compared to those outside clusters. Space–time analysis revealed temporal variation, with PTB clusters persisting from 2011 to 2014 and SGA clusters extending through 2017. Compared to low-risk clusters, high-risk clusters had younger birthing individuals (mean age ~26.5 vs. ~33 years), lower maternal college degree attainment (~20% vs. ~80%), higher rates of late or no prenatal care (~16% vs. 11%), and increased prevalence of smoking and hypertension (all P < 0.001). Community-level indicators showed lower median household incomes ($\$40,000$ vs. ~$\$105,000$), greater poverty (~16% vs. ~7% below $\$10,000$/year), higher public assistance use (~32% vs. ~5%), and reduced healthcare access (greater distances to emergency and specialty care) in high-risk areas (all P < 0.001). Neighborhood deprivation indices were significantly elevated, commutes were longer, and population density was lower in these clusters. These findings highlight that adverse birth outcomes cluster in neighborhoods with pronounced socioeconomic and health disparities. Conclusion: High-risk birth clusters highlight intertwined factors: individual, socio-economic, and geographic. Addressing these requires comprehensive interventions focusing on social and structural determinants of health.

Birth outcomes↗
DOE OSTI2023

A new approach of microbiome monitoring in the built environment: feasibility analysis of condensation capture

Background: Humans emit approximately 30 million microbial cells per hour into their immediate vicinity. However, sampling of aerosolized microbial taxa (aerobiome) remains largely uncharacterized due to the complexity and limitations of sampling techniques, which are highly susceptible to low biomass and rapid sample degradation. Recently, there has been an interest in developing technology that collects naturally occurring water from the atmosphere, even within the built environment. Here, we analyze the feasibility of indoor aerosol condensation collection as a method to capture and analyze the aerobiome. Methods: Aerosols were collected via condensation or active impingement in a laboratory setting over the course of 8 h. Microbial DNA was extracted from collected samples and sequenced (16S rRNA) to analyze microbial diversity and community composition. Dimensional reduction and multivariate statistics were employed to identify significant (p < 0.05) differences in relative abundances of specific microbial taxa observed between the two sampling platforms. Results: Aerosol condensation capture is highly efficient with a yield greater than 95% when compared to expected values. Compared to air impingement, aerosol condensation showed no significant difference (ANOVA, p > 0.05) in microbial diversity. Among identified taxa, Streptophyta and Pseudomonadales comprised approximately 70% of the microbial community composition. Conclusion: The results suggest that condensation of atmospheric humidity is a suitable method for the capture of airborne microbial taxa reflected by microbial community similarity between devices. Future investigation of aerosol condensation may provide insight into the efficacy and viability of this new tool to investigate airborne microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Method to Investigate Cognitive Aging Effects in Nuclear Operations Using the Rancor Microworld Simulator

Many nuclear power plant (NPP) operators are close to, or at retirement age. Since NPP operators are responsible for tasks that engage different and complex cognitive abilities, human factors researchers recently called for the need to examine cognitive aging effects and potential interactions with modernized control rooms. One way control rooms are being modernized is by replacing paper-based procedures with computer-based procedures (CBPs). This paper outlines a method for testing preference and usability of three types of CBPs as a function of age, in simulated NPP operations. The Rancor Microworld Simulator will be used, which mimics an NPP control room, allowing novices to learn to perform operator tasks quickly. The experiment will have two independent variables: age (young and old) and CBP-type (Type 1, Type 2, and Type 3). The three types of CBPs vary in levels of embedded instrumentation and controls. Adults aged 18–25 years (young group) and =60 years (older group) will be recruited from the general population. The dependent variables will be preference and usability metrics via survey and simulator logs. All participants will perform two scenario types (startup and loss of feedwater operations) and will be randomly assigned to one type of CBP to guide them through the scenario. Analyses of variance (ANOVAs) will be used to determine whether there is a main effect of age and any age x CBP-type interactions on the preference and usability dependent variables.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Effect of biochanin A on the rumen microbial community of Holstein steers consuming a high fiber diet and subjected to a subacute acidosis challenge

Subacute rumen acidosis (SARA) occurs when highly fermentable carbohydrates are introduced into the diet, decreasing pH and disturbing the microbial ecology of the rumen. Rumen amylolytic bacteria rapidly catabolize starch, fermentation acids accumulate in the rumen and reduce environmental pH. Historically, antibiotics ( e . g ., monensin, MON) have been used in the prevention and treatment of SARA. Biochanin A (BCA), an isoflavone produced by red clover ( Trifolium pratense ), mitigates changes associated with starch fermentation ex vivo . The objective of the study was to determine the effect of BCA on amylolytic bacteria and rumen pH during a SARA challenge. Twelve rumen fistulated steers were assigned to 1 of 4 treatments: HF CON (high fiber control), SARA CON, MON (200 mg d -1 ), or BCA (6 g d -1 ). The basal diet consisted of corn silage and dried distiller’s grains ad libitum . The study consisted of a 2-wk adaptation, a 1-wk HF period, and an 8-d SARA challenge (d 1–4: 40% corn; d 5–8: 70% cracked corn). Samples for pH and enumeration were taken on the last day of each period (4 h). Amylolytic, cellulolytic, and amino acid/peptide-fermenting bacteria (APB) were enumerated. Enumeration data were normalized by log transformation and data were analyzed by repeated measures ANOVA using the MIXED procedure of SAS. The SARA challenge increased total amylolytics and APB, but decreased pH, cellulolytics, and in situ DMD of hay (P < 0.05). BCA treatment counteracted the pH, microbiological, and fermentative changes associated with SARA challenge (P < 0.05). Similar results were also observed with MON (P < 0.05). These results indicate that BCA may be an effective alternative to antibiotics for mitigating SARA in cattle production systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Associations between regional blood-brain barrier permeability, aging, and Alzheimer’s disease biomarkers in cognitively normal older adults

Background Increased blood-brain barrier permeability (BBBp) has been hypothesized as a feature of aging that may lead to the development of Alzheimer’s disease (AD). We sought to identify the brain regions most vulnerable to greater BBBp during aging and examine their regional relationship with neuroimaging biomarkers of AD. Methods We studied 31 cognitively normal older adults (OA) and 10 young adults (YA) from the Berkeley Aging Cohort Study (BACS). Both OA and YA received dynamic contrast-enhanced MRI (DCE-MRI) to quantify K trans values, as a measure of BBBp, in 37 brain regions across the cortex. The OA also received Pittsburgh compound B (PiB)-PET to create distribution volume ratios (DVR) images and flortaucipir (FTP)- PET to create partial volume corrected standardized uptake volume ratios (SUVR) images. Repeated measures ANOVA assessed the brain regions where OA showed greater BBBp than YA. In OA, K trans values were compared based on sex, Aβ positivity status, and APOE4carrier status within a composite region across the areas susceptible to aging. We used linear models and sparse canonical correlation analysis (SCCA) to examine the relationship between K trans and AD biomarkers. Results OA showed greater BBBp than YA predominately in the temporal lobe, with some involvement of parietal, occipital and frontal lobes. Within an averaged ROI of affected regions, there was no difference in K trans values based on sex or Aβ positivity, but OA who were APOE4carriers had significantly higher K trans values. There was no direct relationship between averaged K trans and global Aβ pathology, but there was a trend for an Ab status by tau interaction on K trans in this region. SCCA showed increased K trans was associated with increased PiB DVR, mainly in temporal and parietal brain regions. There was not a significant relationship between K trans and FTP SUVR. Discussion Our findings indicate that the BBB shows regional vulnerability during normal aging that overlaps considerably with the pattern of AD pathology. Greater BBBp in brain regions affected in aging is related to APOE genotype and may also be related to the pathological accumulation of Aβ.

Science & Technology - Other Topics↗
DOE OSTI2023

Neutrino Scattering in the NOvA Near Detector

aNOvA is a long-baseline accelerator-based neutrino experiment based in the USA. NOvA uses an intense neutrino beam produced at Fermilab’s accelerator complex to make physics measurements of neutrino oscillations, neutrino cross sections, and much more. For its physics goals, NOvA uses two functionally-identical detectors. The Near Detector (ND) is situated at Fermilab, 1 km from the neutrino target and the Far Detector (FD) is located at Ash River, MN, a distance of 810 km from the neutrino source. The ND receives a high statistics neutrino flux which gives a unique opportunity for high-precision neutrino cross-section measurements and is used as a control for the oscillation analyses. In this talk/poster, I will give an overview of the NOvA experiment. I will also talk about the current and future status of the NOvA’s cross-section physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™

Ultra-high performance liquid chromatography–high-resolution mass spectrometry (UPHLC–HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000’s of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

environmental monitoring↗
DOE OSTI2022

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.

Woods, Patrick↗
DOE OSTI2022

The effects of 8-week complex training on lower-limb strength and power of Chinese elite female modern pentathlon athletes

Complex training (CT) is a combination training method that alternates between performing high-load resistance training (RT) and plyometric training within one single session. The study aimed to examine the effects of CT on lower-limb strength and power of elite female modern pentathlon athletes under the new modern pentathlon format and competition rules. Ten female participants (age: 23.55 ± 2.22 years, weight: 60.59 ± 3.87 kg, height: 169.44 ± 4.57 cm, and training experience: 6.90 ± 2.08 years) of the national modern pentathlon team completed 8 weeks of RT as followed by 8 weeks of CT, with 2 weeks of break. Then, the participants conducted 8 weeks of CT, which included RT combined with plyometric training (e.g., drop jump and continuous jump). All stages of training were designed by the linear strength training period theories, requiring participants to train twice for the first 4 weeks and three times for the second 4 weeks. The one-repetition maximum (1RM) of squat, isometric mid-thigh pull (IMTP), counter-movement jump (CMJ), squat jump (SJ), pre-stretch augmentation percentage (PSAP), and reaction strength index (RSI) were assessed before and after both RT and CT training. One-way repeated-measure ANOVA models revealed that the 1RM of squat was significantly improved ( p < 0.001) after RT as compared to pre-RT. No significant improvement in IMTP ( p = 0.055), CMJ ( p = 0.194), SJ ( p = 0.692), PSAP ( p = 0.087), and RSI ( p = 0.238) was not observed. After CT, 1RM of squat ( p < 0.001), IMTP ( p < 0.035), CMJ ( p < 0.001), SJ ( p < 0.008), RSI ( p < 0.006) were significant improved as compared to pre-RT, post-RT and pre-CT, while significant improvements in PSAP were observed as compared to pre-RT ( p = 0.003) and pre-CT ( p = 0.027), but not to post-RT ( p = 0.156). This pilot study showed the promise of CT following RT to improve lower-limb strength and power in elite female modern pentathlon athletes. The findings are worthwhile to be confirmed in future studies with larger sample size and randomized design.

Qiao, Zining↗
DOE OSTI2021

Experimental Studies on Wood Pellets Combustion in a Fixed Bed Combustor Using Taguchi Method

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature.

Castro, Carlos↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗