SEARCH · Search NASA

Results for “Reliability Prediction”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,675 records · Page 93

All sources NASA NTRS DOE OSTI

DOE OSTI2022

MODELING AND PARAMETRIC STUDY OF END-GAS AUTOIGNITION TO ALLOW THE REALIZATION OF ULTRA-LOW EMISSIONS, HIGH-EFFICIENCY HEAVY-DUTY SPARK-IGNITED NATURAL GAS ENGINES

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the low emission limits and the wide range of chemical reactivity in pipeline-quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its Methane Number (MN), which is analogous to the Octane Number for liquid SI fuels, can span from 65 to 95. In order to realize diesel-like efficiencies, SI NG engines must be designed to operate at high Brake Mean Effective Pressures (BMEP), near or beyond knock limits, over a wide range of fuel reactivity. This requires a deep understanding of the combustion-engine interactions pertaining to flame propagation and End-Gas Autoignition (EGAI), i.e., the autoignition of the unburned gas (end gas) ahead of the flame front. However, EGAI, if controlled, provides an opportunity to increase SI NG engine efficiency by increasing the combustion rate and the total fraction of burned fuel, mitigating the effects of the slow flame speeds characteristic of natural gas fuels, which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, to realize diesel-like efficiencies and ultra-low emissions on SI NG engines, this work proposes the study of the main parameters influencing the modeling and prediction of NG EGAI to allow for its control. In this work, a novel EGAI detection and onset determination method was developed to reliably quantify EGAI for data analysis and engine control. The new method allowed the prediction of EGAI on SI NG engines without the need to use engine- and operating-condition-dependent thresholds and reduced the error in quantifying the fraction of the total energy released by the EGAI event by up to 40%pts. One- and three-dimensional engine models were then developed to study the engine/fuel interactions that lead to NG EGAI and its performance benefits. These models, although having decent agreement with experimental data, showed the need to account for NOx chemistry when predicting NG EGAI due to a consistently later prediction of the EGAI onset (~1.65 crank-angle degrees) and thus, a new reduced chemical mechanism for real NG fuels was developed containing NOx chemistry. The new reduced mechanism improved the EGAI onset prediction agreement to within ±0.5 crank-angle degrees and decreased simulation time during combustion by nearly 50% when using the further reduced AREIS50NOx chemical mechanism. These models were then used to study the role of NG composition on EGAI, evaluate the engine/fuel interactions leading to NG EGAI, and perform engine optimization while leveraging EGAI to increase thermal efficiency. Piston design optimization combined with a Controlled EGAI (C-EGAI) combustion mode allowed a Heavy-Duty (HD) SI NG engine to operate at diesel-like efficiencies, i.e., Brake Thermal Efficiency (BTE) ≥44%. Experimental and modeling data analysis revealed that earlier and faster heat release increases combustion efficiency by an average of 1%pts, increases work transferred to the piston resulting in a decrease in exhaust losses by 50% depending on the engine operating condition while slightly increasing heat losses. Finally, the simulation results revealed an opportunity to further enhance the BTE (up to 50%) by enabling C-EGAI combustion at leaner conditions, λ=1.4-1.6.

Bestel, Diego Bernardi↗

NASA NTRS2019

Application of Synoptic Magnetograms for Prediction of Solar Activity Using Ensemble Kalman Filter

Solar activity predictions using the data assimilation approach have demonstrated great potential to build reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to a non-linear dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. These developments assume an empirical relationship between the mean toroidal magnetic field flux and the sunspot number. Estimated from the sunspot number series, variations of the toroidal field have been used to assimilate the data into the Parker-Kleeorin-Ruzmakin (PKR) dynamo model by applying the EnKF method. The dynamo model describes the evolution of the toroidal and poloidal components of the magnetic field and the magnetic helicity. Full-disk magnetograms provide more accurate and complete input data by constraining both the toroidal and poloidal global field components, but these data are available only for the last four solar cycles. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of limited time series observations on the accuracy of solar activity predictions. We present EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach.

Kitiashvili, Irina N.↗

DOE OSTI2023

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Markov reliability models for digital flight control systems

The reliability of digital flight control systems can often be accurately predicted using Markov chain models. The cost of numerical solution depends on a model's size and stiffness. Acyclic Markov models, a useful special case, are particularly amenable to efficient numerical solution. Even in the general case, instantaneous coverage approximation allows the reduction of some cyclic models to more readily solvable acyclic models. After considering the solution of single-phase models, the discussion is extended to phased-mission models. Phased-mission reliability models are classified based on the state restoration behavior that occurs between mission phases. As an economical approach for the solution of such models, the mean failure rate solution method is introduced. A numerical example is used to show the influence of fault-model parameters and interphase behavior on system unreliability.

Mcgough, John↗

NASA NTRS1998

Deterministic Multiaxial Creep and Creep Rupture Enhancements for CARES/Creep Integrated Design Code

High temperature and long duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. A previous model advanced by the authors described a methodology by which the creep rupture life of a loaded component can be predicted. That model was based on the life fraction damage accumulation rule in association with the modified Monkman-Grant creep rupture criterion. However, that model did not take into account the deteriorating state of the material due to creep damage (e.g., cavitation) as time elapsed. In addition, the material creep parameters used in that life prediction methodology, were based on uniaxial creep curves displaying primary and secondary creep behavior, with no tertiary regime. The objective of this paper is to present a creep life prediction methodology based on a modified form of the Kachanov-Rabotnov continuum damage mechanics (CDM) theory. In this theory, the uniaxial creep rate is described in terms of sum, temperature, time, and the current state of material damage. This scalar damage state parameter is basically an abstract measure of the current state of material damage due to creep deformation. The damage rate is assumed to vary with stress, temperature, time, and the current state of damage itself. Multiaxial creep and creep rupture formulations of the CDM approach are presented in this paper. Parameter estimation methodologies based on nonlinear regression analysis are also described for both, isothermal constant stress states and anisothermal variable stress conditions This creep life prediction methodology was preliminarily added to the integrated design code CARES/Creep (Ceramics Analysis and Reliability Evaluation of Structures/Creep), which is a postprocessor program to commercially available finite element analysis (FEA) packages. Two examples, showing comparisons between experimental and predicted creep lives of ceramic specimens, are used to demonstrate the viability of Ns methodology and the CARES/Creep program.

Jadaan, Osama M.↗

NASA NTRS1998

Deterministic and Probabilistic Creep and Creep Rupture Enhancement to CARES/Creep: Multiaxial Creep Life Prediction of Ceramic Structures Using Continuum Damage Mechanics and the Finite Element Method

High temperature and long duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. A previous model advanced by the authors described a methodology by which the creep rupture life of a loaded component can be predicted. That model was based on the life fraction damage accumulation rule in association with the modified Monkman-Grant creep ripture criterion However, that model did not take into account the deteriorating state of the material due to creep damage (e.g., cavitation) as time elapsed. In addition, the material creep parameters used in that life prediction methodology, were based on uniaxial creep curves displaying primary and secondary creep behavior, with no tertiary regime. The objective of this paper is to present a creep life prediction methodology based on a modified form of the Kachanov-Rabotnov continuum damage mechanics (CDM) theory. In this theory, the uniaxial creep rate is described in terms of stress, temperature, time, and the current state of material damage. This scalar damage state parameter is basically an abstract measure of the current state of material damage due to creep deformation. The damage rate is assumed to vary with stress, temperature, time, and the current state of damage itself. Multiaxial creep and creep rupture formulations of the CDM approach are presented in this paper. Parameter estimation methodologies based on nonlinear regression analysis are also described for both, isothermal constant stress states and anisothermal variable stress conditions This creep life prediction methodology was preliminarily added to the integrated design code CARES/Creep (Ceramics Analysis and Reliability Evaluation of Structures/Creep), which is a postprocessor program to commercially available finite element analysis (FEA) packages. Two examples, showing comparisons between experimental and predicted creep lives of ceramic specimens, are used to demonstrate the viability of this methodology and the CARES/Creep program.

Jadaan, Osama M.↗

DOE OSTI2017

Cost-Aware Cloud Profiling, Prediction, and Provisioning as a Service

Here we present Scalable Cost-Aware Cloud Infrastructure Management and Provisioning (SCRIMP)- a service-based system that enables application developers and users to reliably outsource the task of provisioning cloud infrastructure. We show that by understanding application requirements, predicting dynamic market conditions, and automatically provisioning infrastructure according to user-defined policies and real-time conditions that our approaches can reduce costs by an order of magnitude when using commercial clouds while also improving execution performance and efficiency.

Chard, Ryan↗

DOE OSTI2020

Prediction of Hydroxymethylfurfural Yield in Glucose Conversion through Investigation of Lewis Acid and Organic Solvent Effects

Hydroxymethylfurfural (HMF) is one of the important renewable platform compounds that can be obtained from biomass feedstocks through glucose conversion catalyzed by Brønsted and Lewis acids. However, it is challenging to enhance the HMF yield due to side reactions. In this study, a systematic approach combining theory and experiment was performed to investigate the influence of Lewis acids and organic solvents on the HMF yield. For the Lewis acid effect, a relationship between chemical hardness and experimental HMF yields was found in the rate-limiting step of glucose-to-fructose isomerization for six metal chlorides; HMF production was promoted when the metal chloride and a substrate had a similar chemical hardness. To study the organic solvent effect, a multivariate model was developed based on the insights gained from the mechanistic study of fructose dehydration, to predict HMF yields in a given water-organic cosolvent system. It showed a reliable accuracy in evaluating HMF yields with a mean absolute error (MAE) of 3.0% with respect to experimental HMF yields for 13 solvents, and also predicted HMF yields with a MAE of 10.7% for four new solvents. Chemical interpretation of the model revealed that it is desirable to use a solvent capable of stabilizing the carbocation intermediates with low proton transfer activity and high hydrogen bond basicity, to maximize the HMF yield. This multivariate model informs experimentalists about rational selection of solvents with very low computational costs needed to calculate only six variables for each solvent. It can be expanded to other catalytic systems such as heterogeneous Brønsted–Lewis bifunctional catalysts and enables optimization of reaction conditions to obtain other useful platform molecules through biomass conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Impacts on product quality attributes of monoclonal antibodies produced in CHO cell bioreactor cultures during intentional mycoplasma contamination events

Abstract A mycoplasma contamination event in a biomanufacturing facility can result in costly cleanups and potential drug shortages. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and penetrate the standard 0.2‐µm filters used in the clarification of harvested cell culture fluid. Previously, we reported a study regarding the ability of Mycoplasma arginini to persist in a single‐use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G 1 (IgG1) antibody. Our previous work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Careful evaluation of certain identified process parameters over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before detection from a traditional method. In this report, we studied the changes in the IgG1 product quality produced by CHO cells considered to be induced by the M. arginini contamination events. We observed changes in critical quality attributes correlated with the duration of contamination, including increased acidic charge variants and high mannose species, which were further modeled using principal component analysis to explore the relationships among M. arginini contamination, CHO cell growth and metabolites, and IgG1 product quality attributes. Finally, partial least square models using NIR spectral data were used to establish predictions of high levels (≥10 4 colony‐forming unit [CFU/ml]) of M. arginini contamination, but prediction of levels below 10 4 CFU/ml were not reliable. Contamination of CHO cells with M. arginini resulted in significant reduction of antibody product quality, highlighting the importance of rapid microbiological testing and mycoplasma testing during particularly long upstream bioprocesses to ensure product safety and quality.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Kinetic Modeling of a Silicon Refining Process in a Moist Hydrogen Atmosphere

We developed a kinetic model that considers both silicon loss and boron removal in a metallurgical grade silicon refining process. This model was based on the hypotheses of reversible reactions. The reaction rate coefficient kept the same form but error of terminal boron concentration could be introduced when relating irreversible reactions. Experimental data from published studies were used to develop a model that fit the existing data. At 1500 °C, our kinetic analysis suggested that refining silicon in a moist hydrogen atmosphere generates several primary volatile species, including SiO, SiH, HBO, and HBO{sub 2}. Using the experimental data and the kinetic analysis of volatile species, we developed a model that predicts a linear relationship between the reaction rate coefficient k and both the quadratic function of p(H{sub 2}O) and the square root of p(H{sub 2}). Moreover, the model predicted the partial pressure values for the predominant volatile species and the prediction was confirmed by the thermodynamic calculations, indicating the reliability of the model. We believe this model provides a foundation for designing a silicon refining process with a fast boron removal rate and low silicon loss.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Kinetic isotope effects for dissociative recombination of tritiated ketenyl ion ( 3 HCCO + ): A surface-hopping ab initio molecular dynamics study

Dissociative recombination (DR) reactions are important when modeling charged species in the presence of free electrons. While experimental measurements of DR reaction rates are challenging, surface hopping ab initio molecular dynamics (SH-AIMD) simulations provide an attractive alternative. SH-AIMD is especially well-suited for estimating branching ratios, i.e., the relative rates of competing production channels, for DR reactions. Although the radiolysis of diatomic tritium has been studied experimentally, previous attempts to model these systems have failed to account for isotope effects in DR reactions. Previous SH-AIMD studies have also not investigated tritium isotope effects for the branching ratios of DR reactions. In this study, we compute the DR branching ratios of the protiated and tritiated ketenyl ion. Comparison with literature values for the protiated branching ratios provides confidence in the reliability of our SH-AIMD results. Our simulations predict a significant increase of the HC + CO branching ratio for the tritiated system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Assessing thermal comfort and participation in residential demand flexibility programs

Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging due to the variability in household energy use behaviors. Current estimates show that only 50 % of projected savings from DF resources are actualized due to regulatory, technological, and social barriers. From a household perspective, concerns over thermal comfort during space conditioning-based DF events significantly impact participation decisions. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. Here, the proposed method was applied to a heat pump DF field study in Cordova, Alaska. The study’s key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22 °C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available through utility and device-level energy use data. Thus, it can supplement these sources and help program administrators develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.

Demand side management↗

DOE OSTI2016

Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions

The growing need for uncertainty analysis of complex computational models has led to an expanding use of meta-models across engineering and sciences. The efficiency of meta-modeling techniques relies on their ability to provide statistically-equivalent analytical representations based on relatively few evaluations of the original model. Polynomial chaos expansions (PCE) have proven a powerful tool for developing meta-models in a wide range of applications; the key idea thereof is to expand the model response onto a basis made of multivariate polynomials obtained as tensor products of appropriate univariate polynomials. The classical PCE approach nevertheless faces the “curse of dimensionality”, namely the exponential increase of the basis size with increasing input dimension. To address this limitation, the sparse PCE technique has been proposed, in which the expansion is carried out on only a few relevant basis terms that are automatically selected by a suitable algorithm. An alternative for developing meta-models with polynomial functions in high-dimensional problems is offered by the newly emerged low-rank approximations (LRA) approach. By exploiting the tensor–product structure of the multivariate basis, LRA can provide polynomial representations in highly compressed formats. Through extensive numerical investigations, we herein first shed light on issues relating to the construction of canonical LRA with a particular greedy algorithm involving a sequential updating of the polynomial coefficients along separate dimensions. Specifically, we examine the selection of optimal rank, stopping criteria in the updating of the polynomial coefficients and error estimation. In the sequel, we confront canonical LRA to sparse PCE in structural-mechanics and heat-conduction applications based on finite-element solutions. Canonical LRA exhibit smaller errors than sparse PCE in cases when the number of available model evaluations is small with respect to the input dimension, a situation that is often encountered in real-life problems. By introducing the conditional generalization error, we further demonstrate that canonical LRA tend to outperform sparse PCE in the prediction of extreme model responses, which is critical in reliability analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Genome-Wide Tuning of Protein Expression Levels to Rapidly Engineer Microbial Traits

The reliable engineering of biological systems requires quantitative mapping of predictable and context-independent expression over a broad range of protein expression levels. However, current techniques for modifying expression levels are cumbersome and are not amenable to high-throughput approaches. Here we present major improvements to current techniques through the design and construction of E. coli genome-wide libraries using synthetic DNA cassettes that can tune expression over a ~10 4 range. The cassettes also contain molecular barcodes that are optimized for next-generation sequencing, enabling rapid and quantitative tracking of alleles that have the highest fitness advantage. We show these libraries can be used to determine which genes and expression levels confer greater fitness to E. coli under different growth conditions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Unravelling donor–acceptor film morphology formation for environmentally-friendly OPV ink formulations

The challenge of coating organic photovoltaics (OPV) from green solvents is to achieve the required nanostructured interpenetrating network of donor and acceptor domains based on a rational choice of solvent approach as opposed to the usual trial-and-error methods. We demonstrate here that we can achieve a bicontinuous interpenetrating network with nanoscale phase separation for the chosen donor–acceptor material system poly[2,3-bis-(3-octyloxyphenyl)quinoxaline-5,8-diyl-alt-thiophene-2,5-diyl]:phenyl-C 61 butyric acid methyl ester (TQ1:PC 61 BM) when processing from green solvent ink formulations. This structure is achieved by first calculating the Hansen solubility parameters (HSP) of the donor and acceptor materials, followed by careful choice of solvents with selective relative solubilities for the two materials based on the desired order of precipitation necessary for forming a nanostructured interdigitated network morphology. We found that the relative distances in Hansen space (R a ) between TQ1 and the primary solvent, on the one hand, and PC 61 BM and the primary solvent, on the other hand, could be correlated to the donor–acceptor morphology for the formulations based on the solvents d-limonene, anisole, and 2-methyl anisole, as well as the halogenated reference solvent o-dichlorobenzene. This nanostructured blend film morphology was characterised with scanning transmission X-ray microscopy (STXM) and transmission electron microscopy (TEM), and the film surface composition was analysed by near edge X-ray absorption fine structure (NEXAFS) spectroscopy. Hansen solubility theory, based on solution thermodynamics, has been used and we propose an HSP-based method that is a general platform for the rational design of ink formulations for solution-based organic electronics, in particular facilitating the green solvent transition of organic photovoltaics. Our results show that the bulk heterojunction morphology for a donor–acceptor system processed from customised solvent mixtures can be predicted by the HSP-based method with good reliability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗

DOE OSTI2025

Evaluating the limitations of Bayesian metabolic control analysis

AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

Shin, Janis (ORCID:0000000216572455)↗

DOE OSTI2026

Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making

Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks—MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

ecosystem modeling↗