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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant charge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. For Li-ion batteries, for example, lithium moving between electrodes during charging and discharging process causes expansion and contraction of grains, and the strongly anisotropic and nonlinearly [Li]-dependent grain material properties can cause grains to expand into and contract away from each other, leading to chemo-mechanical cracking. In the first part of this work, a RKPM based computational framework for solving the coupled solid-phase lithium conservation with Fickian diffusion and the lithium concentration dependent anisotropic mechanical problem subjected to a highly nonlinear Butler-Volmer boundary condition is introduced. The choice of RKPM completeness conditions for lithium concentration and mechanical deformation fields, and the variational consistency condition for the domain integration of the coupled problem is first determined. In the second part of this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material, by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that is necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure.

damage modeling↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2021

Impact of Intake and Exhaust Ducts on the Recovery Efficiency of Heat Recovery Ventilation Systems

The heat recovery efficiency of ventilation systems utilizing heat recovery ventilators (HRVs) depends not only on the heat recovery efficiency of the HRV units themselves but also on the intake and exhaust ducts that connect the HRV units to the outside environment. However, these ducts are often neglected in heat loss calculations, as their impact on the overall heat recovery efficiency of HRV systems is often not understood and, to the knowledge of the authors, a mathematical model for the overall heat recovery efficiency of HRV systems that accounts for these ducts has not been published. In this research, a mathematical model for the overall heat recovery efficiency of HRV systems that accounts for the intake and exhaust ducts was derived and validated using real-life data. The model-predicted decrease in heat recovery efficiency due to the ducts was in reasonable agreement (relative error within 20%) with the real-life measurements. The results suggest that utilizing this model allows for more correct ventilation heat loss calculations compared to using the heat recovery efficiency of the HRV unit alone, but more field studies are needed to verify the accuracy of this model in a wide range of applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Dose Rate Effects on Degradation of Nuclear Power Plant Electrical Cable Insulation at a Common Dose

The intent of this report is to address an identified knowledge gap in relating accelerated aging of nuclear electrical cables to service aging: dose rate effects (DRE). Here, DRE refer to gamma radiation-induced polymer degradation being a function of dose rate in addition to total absorbed dose. The concern raised is that historical qualification conducted at higher dose rates to simulate service lifetime may underestimate insulation degradation that occurs at lower dose rates in service. In the work described herein, common nuclear cable insulation materials—cross-linked polyethylene (XLPE) and ethylene propylene diene elastomer (EPDM)—were subjected to accelerated aging at ambient temperature (26°C) at different gamma dose rates of 100, 200 and 1800 Gy/h for select exposure durations to achieve constant total doses of 170, 210 and 300 kGy to evaluate DRE. First, the cable insulation material types investigated are described. Then, the accelerated aging experimental process involving gamma irradiation applied to the insulation specimens at room temperature and different dose rates is discussed. Then, the experimental characterization techniques used to perform this work are elucidated. These include elongation at break (EAB), mass change, yellowness index (YI), carbonyl index (CI), density, indenter modulus (IM), and relaxation constant (t). Theory of polymer degradation is discussed, and characterization results and discussion are provided. Finally, concluding remarks are made. The findings from this work and cited prior work reveal that DRE are material dependent, even between similar material categories (e.g., XLPE). In the case of the EPDM studied, degradation of ductility was observed to be greater at higher dose rate for the same total dose, indicating accelerated gamma aging to be more conservative than extended aging. Thus, conclusions regarding the conservatism of historical qualification likely require additional consideration for specific materials and conditions in question. The results of this study support the contention that, due to inherent limitations and uncertainties associated with prediction of cable remaining useful life from accelerated aging experiments, trending of installed cable insulation health status will be more practical and useful for safe and efficient cable aging management repair and replace decisions than lifetime prediction from historical qualification. The combination of material robustness demonstrated by the qualification process and ongoing monitoring of cable health status combine to provide confidence in continued safe use of existing nuclear cables. Additional research into effective and efficient condition monitoring methods for non-destructive evaluation of installed cables is needed to support aging cable management, including material studies to inform interpretation of measured results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Modeling of gap conductance for LWR fuel rods applied in the BISON code

A new gap conductance model is proposed in this study as a combination of Toptan’s model and the Ross-Stoute model. A variance-based sensitivity analysis is performed to understand how simulation results depend on all input parameters of the proposed model. Additionally, new modeling options (e.g. fill gas thermal conductivity, temperature jump distance, thermal accommodation coefficient, etc.) are added into the nuclear fuel performance code, BISON. The need for further investigation of the gap heat transfer between fuel and cladding in BISON motivated this study to evaluate its impact on the code’s predictions. New gap conductance modeling is proposed. A series of integral-effects validation tests is performed: (1) to demonstrate the impact of the proposed model on the code’s fuel temperature predictions at the beginning of life and through the reactor’s life; (2) to ensure that the proposed model is capable of accurately modeling gap heat transfer characteristics in real-world problems; and (3) to investigate the impact of the estimation of fission gas release on the fuel temperature predictions with the proposed model. Overall, the results indicate that the proposed gap conductance model improves BISON’s predictions.

42 ENGINEERING↗
DOE OSTI2025

In-flight performance of the XRISM/Resolve detector system

The Resolve instrument was launched on board the XRISM observatory in early September 2023. The Resolve spectrometer is based on a high-sensitivity X-ray calorimeter detector system (DS) that has been successfully deployed in many ground and sub-orbital spectrometers. However, the Resolve instrument is the first long-term implementation in space. The instrument will provide essential diagnostics for nearly every class of X-ray emitting objects, from galactic supernova remnants to the outskirts of galaxy clusters, without degradation for spatially extended objects. The Resolve DS consists of a 36-pixel microcalorimeter array operated at a heat sink temperature of 50 mK. In pre-flight testing, the DS demonstrated a resolving power of better than 1300 at 6 keV with a simultaneous bandpass from below 0.3 keV to above 12 keV and a timing precision better than 100μs. An anti-coincidence detector placed directly behind the microcalorimeter array effectively suppresses background. The detector energy-resolution budget included terms for interference from the Resolve cooling system and the spacecraft. Additional terms for energy-scale stability, on-orbit effects, and use of mid-grade events were also included, predicting an end-of-life, on-orbit performance for high- and mid-resolution grade events that meet the requirement of 7 eV FWHM at 6 keV. Here, we discuss the actual on-orbit performance of the Resolve DS and compare this with the performance in pre-flight testing, on-orbit predictions, and the almost identical Hitomi/SXS instrument. We will also discuss the on-orbit gain stability, an assessment of on-orbit interference, and measurements of the on-orbit background.

Astronomy and AstroPhysics↗
DOE OSTI2023

The Neutron Mean Life and Big Bang Nucleosynthesis

We explore the effect of neutron lifetime and its uncertainty on standard big bang nucleosynthesis (BBN). BBN describes the cosmic production of the light nuclides, 1H, D, 3H+3He, 4He, and 7Li+7Be, in the first minutes of cosmic time. The neutron mean life τn has two roles in modern BBN calculations: (1) it normalizes the matrix element for weak n↔p interconversions, and (2) it sets the rate of free neutron decay after the weak interactions freeze-out. We review the history of the interplay between τn measurements and BBN, and present a study of the sensitivity of the light element abundances to the modern neutron lifetime measurements. We find that τn uncertainties dominate the predicted 4He error budget, but these theory errors remain smaller than the uncertainties in 4He observations, even with the dispersion in recent neutron lifetime measurements. For the other light element predictions, τn contributes negligibly to their error budget. Turning the problem around, we combine present BBN and cosmic microwave background (CMB) determinations of the cosmic baryon density to predict a “cosmologically preferred” mean life of τn(BBN+CMB)=870±16s, which is consistent with experimental mean life determinations. We show that if future astronomical and cosmological helium observations can reach an uncertainty of σobs(Yp)=0.001 in the 4He mass fraction Yp, this could begin to discriminate between the mean life determinations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Projected long-term effects of colorectal cancer screening disruptions following the COVID-19 pandemic

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in colorectal cancer (CRC) outcomes due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term CRC outcomes was measured by the number of life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3–18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in CRC outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

MURR LEU structural and thermal hydraulics analyses: Part I – Preliminary irradiation thermo-mechanical behavior

The University of Missouri Research Reactor (MURR) is expected to be converted from highly enriched uranium (HEU, ≥ 20 wt% U-235) U-Al x dispersion fuel to low-enriched uranium (LEU, < 20 wt% U-235) with U-10Mo monolithic fuel. This work introduces high-fidelity irradiation thermo–mechanical (T-M) analysis of the MURR LEU focusing on changes in coolant channel gap thickness. Three-dimensional (3D) finite element (FE) models were developed to simulate the irradiation T-M behavior of the MURR LEU element with all 23 curved fuel plates, the two side plates, and the combs. It was shown that channel gap thickness changes were influenced not only by plate thickness variations due to fuel swelling and creep but also by the radial displacement of consecutive MURR LEU plates. Modeling the fuel element assembly captured side plate displacements, which were shown to reduce radial fuel plate displacements towards the convex side. The maximum local radial displacement in the element was predicted at the end of life (EOL) as 23.7 mil (602.0 µm) on the lateral centerline of plate 23 towards the convex side. The maximum stripe-averaged reduction in channel gap thickness, particularly relevant for thermal hydraulics (TH) safety analysis, was calculated as 15.9 mil (403.9 µm) in single-side heated channel 24 (the outermost channel). These results account for the thermal resistance from the oxide build-up on cladding surfaces which was shown to be up to 0.82 mil (20.8 µm) thick. It was demonstrated that accounting for oxide layer thermal resistance led to a 10 °C higher peak fuel temperature and a 4.4 mil (111.8 µm) greater maximum local radial displacement. The impact of the calculated channel gap thickness changes on the MURR LEU TH safety analysis is evaluated in Part II.

Irradiation thermo-mechanical analysis↗
DOE OSTI2021

AST Development for PEM Electrolyzers [Slides]

The ASTWG charter targets include: Develop ASTs to predict 80,000 hours of life in desired application; and Develop additional ASTs to evaluate different PEM electrolyzer components.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluation of Oxygen Consumption as a Sensitive Measure of Electrical Cable Polymer Insulation Degradation

Polymers used as dielectric materials in nuclear grade electrical cables, such as cross-linked polyethylene (XLPE) and ethylene-propylene rubber (EPR), are long-lived and durable. Estimating the decades-long useful service life of polymers experimentally in a feasible timeframe of weeks or months requires greatly accelerated aging in the laboratory at elevated temperatures and/or gamma dose rates. Many limitations of accelerated aging, such as due to artefacts of the process including diffusion limited oxidation and dose rate effects, have been identified that can make accurate lifetime prediction at service conditions challenging. This can be due to differences in degradation mechanisms between rapid and extended aging or to the presence of induction periods during which early aging is undetectable using common metrics of polymer insulation characterization such as tensile elongation at break. Conceptually, an ideal measure of aging would track clearly and predictably with material changes throughout the lifecycle of the evolving material state from early aging through end of useful life. With such a metric accelerated aging could be performed at much milder conditions, conditions closer to those found in long term service, and end of life calculated from the early data. Oxygen consumption has previously been proposed as a sensitive measure of insulation polymer oxidation that might meet such desirable characteristics. In this work, nuclear cable insulation samples of the two most common types, XLPE and EPR, from two of the most sourced manufacturers, RSCC and The Okonite Company, were subjected to gamma irradiation at dose rates of 100, 200, and 300 Gy/hr for up to 42 days at 26°C. The aged samples were used to evaluate oxygen consumption as a sensitive measure of aging for these materials and these conditions. For comparison and validation, the aged samples were also characterized using the more conventional tests of mass change, density, carbonyl index, and yellowness index. For both materials, oxygen consumption during irradiation was found to track consistently with applied dose—oxygen consumption rate was linear with time. Mass change and carbonyl index were also clearly proportional to exposure dose. Yellowness index, a surface sensitive measure, was observed to increase linearly with dose at low exposure levels before plateauing. Sample density was not observed to change under the conditions explored. Oxygen consumption was found to be a particularly sensitive metric to measure the degradation of polymeric cable insulation exposed to radiation. Oxygen consumption was compared to Arrhenius and time-temperature-superposition methods for activation energy calculation using literature date and found to be a useful and viable alternative for lifetime prediction. The techniques developed in this work are anticipated to be valuable in the estimation of insulation aging rates under mild conditions approaching service conditions. Direction comparison of useful life prediction by oxygen consumption with prediction via Arrhenius methodology for thermal aging and existing methods for radiation and combined thermal/radiation aging would serve to further validate this promising approach and refine our understanding of material-specific aging rates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-driven closure modeling for hypersonic turbulent flows

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This report summarizes work performed from FY22-FY24 focused on improving RANS models for hypersonic flows using data-driven modeling and scientific machine learning. In this work we: 1. Investigate the current capabilities of RANS models in Sandia’s parallel aerodynamics and re-entry code (SPARC) for hypersonic flows with a focus on shock boundary layer interactions (SBLIs), 2. Assess several established corrections that exist in the literature aimed at improving predictions for SBLIs, 3. Develop improved models for the Reynolds stress tensor using tensor-basis neural networks, 4. Develop a neural-network-based variable turbulent Prandtl number model to reduce errors in wall heating in SBLIs. 5. Begin future investigations including employing the LIFE framework to improve wall heating predictions in SBLIs as well as the ensemble Kalman filter. We find that current RANS models in SPARC are deficient for complex SBLI flows. In particular, no current model jointly predicts wall heat flux, wall shear stress, and wall pressure with reasonable accuracy. Existing corrections help, but do not alleviate this issue altogether. The development of improved models for the Reynolds stress tensor via tensor-basis neural networks results in more predictive RANS models across a suite of low-speed and high-speed cases. For hypersonic boundary layers, the inclusion of the wall-normal Reynolds stress via TBNNs has an appreciable impact on the wall-normal momentum balance and wall quantities. However, we find that improvements to the Reynolds stress tensor do not address the over-prediction in wall heat flux in SBLIs. We find that a neural-network-based variable turbulent Prandtl number model systematically and substantially improves wall heating predictions for a range of SBLI cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity

The nEXO neutrinoless double beta (0νββ) decay experiment is designed to use a time projection chamber and 5000 kg of isotopically enriched liquid xenon to search for the decay in 136 Xe. Progress in the detector design, paired with higher fidelity in its simulation and an advanced data analysis, based on the one used for the final results of EXO-200, produce a sensitivity prediction that exceeds the half-life of 10 28 years. Specifically, improvements have been made in the understanding of production of scintillation photons and charge as well as of their transport and reconstruction in the detector. The more detailed knowledge of the detector construction has been paired with more assays for trace radioactivity in different materials. In particular, the use of custom electroformed copper is now incorporated in the design, leading to a substantial reduction in backgrounds from the intrinsic radioactivity of detector materials. Furthermore, a number of assumptions from previous sensitivity projections have gained further support from interim work validating the nEXO experiment concept. Together these improvements and updates suggest that the nEXO experiment will reach a half-life sensitivity of 1.35 × 10 28 yr at 90% confidence level in 10 years of data taking, covering the parameter space associated with the inverted neutrino mass ordering, along with a significant portion of the parameter space for the normal ordering scenario, for almost all nuclear matrix elements. Furthermore, the effects of backgrounds deviating from the nominal values used for the projections are also illustrated, concluding that the nEXO design is robust against a number of imperfections of the model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

60 APPLIED LIFE SCIENCES↗