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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2022

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Interim PET After Two ABVD Cycles in Early-Stage Hodgkin Lymphoma: Outcomes Following the Continuation of Chemotherapy Plus Radiotherapy

Purpose: This multicenter retrospective study was designed to evaluate the prognostic role of interim fluorodeoxyglucose-labeled positron emission tomography (i-FDG-PET) in a cohort of patients affected with early-stage Hodgkin lymphoma (HL) treated initially with adriamycin, bleomycin, vinblastine, dacarbazine (ABVD) chemotherapy followed by radiation therapy, and to assess the role of chemotherapy continuation plus radiation therapy for i-FDG-PET-positive patients. Methods and Materials: Data from 257 patients were retrieved from 4 hematology and radiation oncology departments. Inclusion criteria were stage I to IIAB HL, “intention-to-treat” AVBD plus radiation therapy, and FDG-PET at diagnosis and after the first 2 ABVD cycles. All i-FDG-PET scans underwent blinded local review by using the Deauville 5-point scoring system; patients were stratified as negative or positive using 2 Deauville score cutoff values, ≥3 or ≥4. Results: Median follow-up time was 56 months (range: 9-163 months); 5-year overall survival (OS) and disease-specific survival (DSS) for the whole cohort were 97.5% and 98.3%, respectively. Five-year progression-free survival (PFS) was 95.6%. After i-FDG-PET revision, 43 of 257 patients (16.7%) had a positive i-FDG-PET (Deauville scores: 3-5). Five-year PFS rates for i-FDG-PET-negative and i-FDG-PET-positive patients were 98.1% and 83.7%, respectively, if using a Deauville score cutoff of 3, and 97.7% and 78.6%, respectively, if using a cutoff of 4 (P=.0001). Five-year OS for i-FDG-PET-negative and i-FDG-PET-positive patients was 98.5% and 93.0%, respectively, if using a cutoff of 3, and 98.6% and 89.3%, respectively, if using a cutoff of 4 (P=.029 and P=.002). At univariate regression analysis, i-FDG-PET positivity was associated with worse OS and PFS. At multivariate analysis, performed only for PFS, i-FDG-PET positivity confirmed its negative impact (P=.002). Conclusions: i-FDG-PET is prognostic for PFS and OS in early-stage HL patients treated with combined modality therapy; the continuation of chemotherapy followed by radiation therapy is able to obtain durable, complete remission in most i-FDG-PET-positive patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Detecting Peer-to-Peer Botnets in SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems monitor and control critical infrastructure such as the smart grid. As SCADA systems become increasingly interconnected and adopt more and more cyber-enabled components, the risks of cyber attacks become a major concern. Due to their decentralized organization, peer-to-peer (P2P) botnets are resilient to many existing takedown measures and can be exploited as an effective way to launch cyber attacks on SCADA systems. However, little work has been done to detect P2P botnets in SCADA systems, which carry traffic flows with characteristics significantly different from the Internet. In this paper, we design a P2P-botnet detection method for SCADA systems, leveraging built-in traffic monitoring capabilities of SCADA networking devices. The proposed method consists of two stages. In the first stage, we design a simple feature test to filter out non-P2P hosts, which significantly reduces the data volume for P2P-botnet identification. In the second stage, we jointly consider flow-based and connectivity-based features that effectively set apart bots from benign hosts. We propose to use unsupervised learning for P2P-botnet identification, which not only identifies known P2P botnets but also captures newly emerged ones. Our simulation results show that the proposed system achieves high detection rates with very few false positives. Furthermore, our evaluation shows that the proposed method can detect hosts running P2P SCADA applications that are infected by P2P bots.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC Ta

A void coalescence term was proposed as an addition to the original void nucleation and growth (NAG) model to accurately describe void evolution under dynamic loading. The new model, termed as modified void nucleation and growth model (MNAG model), incorporated analytic equations to explicitly account for the evolution of the void number density and the void volume fraction (damage) during void nucleation, growth, as well as the coalescence stage. The parameters in the MNAG model were fitted to molecular dynamics (MD) shock data for single-crystal and nanocrystalline Ta, and the corresponding nucleation, growth, and coalescence rates were extracted. The results suggested that void nucleation, growth, and coalescence rates were dependent on the orientation as well as grain size. Compared to other models, such as NAG, Cocks–Ashby, Tepla, and Tonks, which were only able to reproduce early or later stage damage evolution, the MNAG model was able to reproduce all stages associated with nucleation, growth, and coalescence. The MNAG model could provide the basis for hydrodynamic simulations to improve the fidelity of the damage nucleation and evolution in 3-D microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Efficient Unsteady Flow Visualization with High-Order Access Dependencies

We present a novel high-order access dependencies based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly-seeded pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.

Zhang, Jiang↗
DOE OSTI2020

Aerodynamic Data Fusion Toward the Digital Twin Paradigm

This paper considers the fusion of two aerodynamic data sets originating from differing types of physical or computer experiments. This paper specifically addresses the fusion of 1) noisy and in-complete fields from wind-tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the true field that best matches measured quantities that serve as the ground truth. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. A fundamental challenge in this problem is that the true field is unknown and cannot be estimated with 100% certainty. A Bayesian framework is employed to infer the true fields conditioned on measured quantities of interest; essentially a statistical correction to the data is performed. The fused data may then be used to construct more accurate surrogate models suitable for early stages of aerospace design. An extension of the proper orthogonal decomposition with constraints is also introduced to solve the same problem. In this work, both methods are demonstrated on fusing the pressure distributions for flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions. Comparison of both methods reveals that the Bayesian method is more robust when data are scarce and capable of also accounting for uncertainties in the data. Furthermore, given adequate data, the proper-orthogonal-decomposition-based and Bayesian approaches lead to surprisingly similar results.

42 ENGINEERING↗
NASA NTRS1977

The use of the LANDSAT data collection system and imagery in reservoir management and operation

The author has identified the following significant results. An increase in the data collection system's (DCS) ability to function in the flood control mission with no additional manpower was demonstrated during the storms which struck New England during April and May of 1975 and August 1976. It was found that for this watershed, creditable flood hydrographs could be generated from DCS data. It was concluded that an ideal DCS for reservoir regulation would draw features from LANDSAT and GOES. MSS grayscale computer printout and a USGS topographic map were compared, yielding an optimum computer classification map of the wetland areas of the Merrimack River estuary. A classification accuracy of 75% was obtained for the wetlands unit, taking into account the misclassified and the unclassified pixels. The MSS band 7 grayscale printouts of the Franklin Falls reservoir showed good agreement to USGS topographic maps in total area of water depicted at the low water reservoir stage and at the maximum inundation level. Preliminary analysis of the LANDSAT digital data using the GISS computer algorithms showed that the radiance of snow cover/vegetation varied from approximately 20 mW/sq cm sr in nonvegetated areas to less than 4 mW/sq cm sr for densely covered forested area.

Cooper, S.↗
DOE OSTI2014

SU-E-J-182: A Feasibility Study Evaluating Automatic Identification of Gross Tumor Volume for Breast Cancer Radiotherapy Using Dynamic Contrast-Enhanced MR Imaging

Purpose: To develop a computerized pharmacokinetic model-free Gross Tumor Volume (GTV) segmentation method based on dynamic contrastenhanced MRI (DCE-MRI) data that can improve physician GTV contouring efficiency. Methods: 12 patients with biopsy-proven early stage breast cancer with post-contrast enhanced DCE-MRI images were analyzed in this study. A fuzzy c-means (FCM) clustering-based method was applied to segment 3D GTV from pre-operative DCE-MRI data. A region of interest (ROI) is selected by a clinician/physicist, and the normalized signal evolution curves were calculated by dividing the signal intensity enhancement value at each voxel by the pre-contrast signal intensity value at the corresponding voxel. Three semi-quantitative metrics were analyzed based on normalized signal evolution curves: initial Area Under signal evolution Curve (iAUC), Immediate Enhancement Ratio (IER), and Variance of Enhancement Slope (VES). The FCM algorithm wass applied to partition ROI voxels into GTV voxels and non-GTV voxels by using three analyzed metrics. The partition map for the smaller cluster is then generated and binarized with an automatically calculated threshold. To reduce spurious structures resulting from background, a labeling operation was performed to keep the largest three-dimensional connected component as the identified target. Basic morphological operations including hole-filling and spur removal were useutilized to improve the target smoothness. Each segmented GTV was compared to that drawn by experienced radiation oncologists. An agreement index was proposed to quantify the overlap between the GTVs identified using two approaches and a thershold value of 0.4 is regarded as acceptable. Results: The GTVs identified by the proposed method were overlapped with the ones drawn by radiation oncologists in all cases, and in 10 out of 12 cases, the agreement indices were above the threshold of 0.4. Conclusion: The proposed automatic segmentation method was shown to be promising and might be used to improve physician contouring efficiency. J Horton receives grant from NIH and Varian Medical Systems; F-F Yin receives grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2018

Performance, Stability and Compatibility of Oxygen/RP-1 Multi-Element Oxidizer-Rich Staged-Combustion Injectors

In 2015 and 2016, the National Aeronautics and Space Administration Marshall Space Flight Center designed, fabricated, assembled and hot-fire tested an oxygen/RP-1 propellant multi-element oxidizer-rich staged-combustion test article. The main objective was to provide thrust chamber combustion stability data as part of the Combustion Stability Tool Development program, although demonstration of performance and compatibility of oxidizer-rich main injectors was also important. Funding was provided by the Air Force Space and Missile Systems Center. Five configurations of main injectors were designed and fabricated, using conventional gas-centered swirl coaxial injector element designs generally similar to those used in oxygen/kerosene oxidizer-rich staged combustion engines such as the Russian RD-180 or NK-33 engines. Variations of element features included element size, recess depth, fuel gap width, and the presence of the sleeve separating the swirling fuel flow from the axial oxidizer flow. Ablative combustion chambers were fabricated based on hardware previously used at the NASA MSFC for testing at similar size and pressure. Existing oxygen/RP-1 oxidizer-rich subscale preburner injectors and hot gas ducts from a previous NASA-funded program were modified for use to supply the oxidizer-rich combustion products to the oxidizer circuit of the main injector of the thrust chamber. Testing of the resulting integrated test article - which included the preburner, inter-connecting hot gas duct, main injector, and ablative combustion chamber - was conducted at Test Stand 116 at the East Test Area of the NASA MSFC. The test article was well instrumented with static and dynamic pressure, temperature, and vibration sensors. This paper presents and discusses all the hot-fire test results of the integrated test article thrust chamber. Eighteen successful hot-fire tests of the integrated rig were conducted. Testing was accomplished with all five of the injector element concepts. Main combustion chamber pressures ranged from 710 to 2350 psia, and main combustion chamber mixture ratios ranged from 2.47 to 2.87. A chamber barrier fuel film coolant of about 2% to 4% of the total fuel flow was used for most tests. Characteristic exhaust velocity efficiency excluding the influence of the fuel film cooling ranged from 91% to 98% of theoretical. All tests of the thrust chamber exhibited stable combustion, even down to 40% of nominal operating pressures. Compatibility of the injector face and combustion chamber walls was acceptable. This paper is a follow-on to publication of preliminary test data presented at the 2016 JANNAF Liquid Propulsion Subcommittee meeting.

Hulka, J. R.↗
DOE OSTI2021

A data engineering approach for sustainable chemical end-of-life management

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. Furthermore, a case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e.g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Critical Experiment Design Phase 1 Report for Integral Request 441

This report documents the analytical methods used in the first phase of critical experiment design (CED-1) conducted as part of integral experiment request (IER) 441. The purpose of IER-441 is to develop a capability to test the epithermal/intermediate cross sections of materials at the Sandia National Laboratories (SNL) critical experiment facility using low enriched uranium oxide fuel. This effort determined that the capability can be achieved using a varying number of test samples placed in a central test region of a hexagonally pitched lattice driven with a more moderated exterior region. In general, the results show that it is possible to develop sufficient worth to measure any nuclear data deficiencies, as well as the ability to shift the neutron spectrum for dysprosium, hafnium, silver, tantalum, indium, cobalt, tungsten, and antimony. There is marginal capability to test manganese due to its relatively low worth at higher boron concentrations. At this stage, there is interest in identifying the material or materials of greatest interest to the nuclear data community so that this project can proceed to CED-2 with a clear goal.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

A Heterogeneity-Aware Task Scheduler for Spark

Big data processing systems such as Spark are employed in an increasing number of diverse applications-such as machine learning, graph computation, and scientific computing-each with dynamic and different resource needs. These applications increasingly run on heterogeneous hardware, e.g., with out-of-core accelerators. However, big data platforms do not factor in the multi-dimensional heterogeneity of applications and hardware. This leads to a fundamental mismatch between the application and hardware characteristics, and the resource scheduling adopted in big data platforms. For example, Hadoop and Spark consider only data locality when assigning tasks to nodes, and typically disregard the hardware capabilities and suitability to specific application requirements. In this paper, we present RUPAM, a heterogeneity-aware task scheduling system for big data platforms, which considers both task-level resource characteristics and underlying hardware characteristics, as well as preserves data locality. RUPAM adopts a simple yet effective heuristic to decide the dominant scheduling factor (e.g., CPU, memory, or I/O), given a task in a particular stage. Our experiments show that RUPAM is able to improve the performance of representative applications by up to 62.3% compared to the standard Spark scheduler.

Computer Science↗
DOE OSTI2013

A Prospective, Multicenter Study of Complementary/Alternative Medicine (CAM) Utilization During Definitive Radiation for Breast Cancer

Purpose: Although complementary and alternative medicine (CAM) utilization in breast cancer patients is reported to be high, there are few data on CAM practices in breast patients specifically during radiation. This prospective, multi-institutional study was conducted to define CAM utilization in breast cancer during definitive radiation. Materials/Methods: A validated CAM instrument with a self-skin assessment was administered to 360 Stage 0-III breast cancer patients from 5 centers during the last week of radiation. All data were analyzed to detect significant differences between users/nonusers. Results: CAM usage was reported in 54% of the study cohort (n=194/360). Of CAM users, 71% reported activity-based CAM (eg, Reiki, meditation), 26% topical CAM, and 45% oral CAM. Only 16% received advice/counseling from naturopathic/homeopathic/medical professionals before initiating CAM. CAM use significantly correlated with higher education level (P<.001), inversely correlated with concomitant hormone/radiation therapy use (P=.010), with a trend toward greater use in younger patients (P=.066). On multivariate analysis, level of education (OR: 6.821, 95% CI: 2.307-20.168, P<.001) and hormones/radiation therapy (OR: 0.573, 95% CI: 0.347-0.949, P=.031) independently predicted for CAM use. Significantly lower skin toxicity scores were reported in CAM users vs nonusers, respectively (mild: 34% vs 25%, severe: 17% vs 29%, P=.017). Conclusion: This is the first prospective study to assess CAM practices in breast patients during radiation, with definition of these practices as the first step for future investigation of CAM/radiation interactions. These results should alert radiation oncologists that a large percentage of breast cancer patients use CAM during radiation without disclosure or consideration for potential interactions, and should encourage increased awareness, communication, and documentation of CAM practices in patients undergoing radiation treatment for breast cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
NASA NTRS2005

Simulation and Analyses of Stage Separation Two-Stage Reusable Launch Vehicles

NASA has initiated the development of methodologies, techniques and tools needed for analysis and simulation of stage separation of next generation reusable launch vehicles. As a part of this activity, ConSep simulation tool is being developed which is a MATLAB-based front-and-back-end to the commercially available ADAMS(registered Trademark) solver, an industry standard package for solving multi-body dynamic problems. This paper discusses the application of ConSep to the simulation and analysis of staging maneuvers of two-stage-to-orbit (TSTO) Bimese reusable launch vehicles, one staging at Mach 3 and the other at Mach 6. The proximity and isolated aerodynamic database were assembled using the data from wind tunnel tests conducted at NASA Langley Research Center. The effects of parametric variations in mass, inertia, flight path angle, altitude from their nominal values at staging were evaluated. Monte Carlo runs were performed for Mach 3 staging to evaluate the sensitivity to uncertainties in aerodynamic coefficients.

Pamadi, Bandu N.↗
NASA NTRS2007

Simulation and Analyses of Stage Separation of Two-Stage Reusable Launch Vehicles

NASA has initiated the development of methodologies, techniques and tools needed for analysis and simulation of stage separation of next generation reusable launch vehicles. As a part of this activity, ConSep simulation tool is being developed which is a MATLAB-based front-and-back-end to the commercially available ADAMS(Registerd TradeMark) solver, an industry standard package for solving multi-body dynamic problems. This paper discusses the application of ConSep to the simulation and analysis of staging maneuvers of two-stage-to-orbit (TSTO) Bimese reusable launch vehicles, one staging at Mach 3 and the other at Mach 6. The proximity and isolated aerodynamic database were assembled using the data from wind tunnel tests conducted at NASA Langley Research Center. The effects of parametric variations in mass, inertia, flight path angle, altitude from their nominal values at staging were evaluated. Monte Carlo runs were performed for Mach 3 staging to evaluate the sensitivity to uncertainties in aerodynamic coefficients.

Pamadi, Bandu N.↗
DOE OSTI2023

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗