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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 OSTI2023

An evaluation of meteorological data prediction over Washington, D.C.: Comparison of DCNet observations and NAM model outputs

This study presents an example of how outputs of operational and readily-available mesoscale numerical models can be adapted to initialize dispersion calculations within the urban surface roughness layer. Three years of urban meteorological observations from central Washington, DC, are compared against forecast outputs of the North American Mesoscale (NAM) model. NAM wind speed predictions underestimate the observations in light winds and overestimate the measurements in high winds. Average wind directions are consistent. However, an adjustment of the predicted direction of the plume by –20° is needed. The uncertainty associated with this adjustment is large in light NAM wind speed with no evident variation by season. The values of the standard deviation of the wind direction, σθ derived from NAM model outputs underestimate the observations by a small amount (about –1.5 to –2.5°). The results presented here indicate that mesoscale numerical model outputs can provide information adequate for dispersion calculations. However, levels of uncertainty associated with implementation of the suggested procedures increase with decreasing wind speed, causing considerable uncertainty in the implementation of adjustments as low wind speed conditions are approached. Results and recommendations reported here should not be extended to other numerical models or other cities without further testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration

This is the final technical report for the SETO-funded VRN3P project (PNNL# 76914). The goal of this project, led by Pacific Northwest National Laboratory (PNNL), in collaboration with Lawrence Livermore National Laboratory (LLNL) and Portland General Electric (PGE), was to develop and validate a deep variational recurrent neural network-based net-load prediction (VRN3P) framework for probabilistic time-series forecasting of day-ahead net-load under high solar penetration scenarios. The project team reports successful design of a novel probabilistic net-load forecasting architecture, comprising of a variational autoencoder and a recurrent neural network, which demonstrates 30% improvement in forecast performance, 60% improvement in training time, and consumes 44% less memory, when compared with conventional baseline models. The team tested the VRN3P model performance on GridLAB-D test-cases representing varying BTM solar penetration levels of 20%, 30%, and 50%, with integrated time-series net-load profiles provided by the utility partner (PGE). The VRN3P model demonstrate <2% hourly MAPE (averaged over the year) for day- ahead net-load forecast on the test scenario with 20% BTM solar. Transfer learning extension of the VRN3P model has demonstrated 8.33× speed-up in training, while still achieving acceptable forecast performance of 2.24% hourly MAPE on the 30% BTM solar penetration test-scenario. A preliminary version of the VRN3P GridAPPS-D™has been developed, along with a web-based interactive user-interface (named ‘Forte’) which has made available on GitHub for public use.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method with regard to predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO, site using any of the multivariate methods because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

17 WIND ENERGY↗
DOE OSTI2016

Exploring the impacts of physics and resolution on aqua-planet simulations from a nonhydrostatic global variable-resolution modeling framework

Advances in computing resources are gradually moving regional and global numerical forecasting simulations towards sub-10 km resolution, but global high resolution climate simulations remain a challenge. The non-hydrostatic Model for Prediction Across Scales (MPAS) provides a global framework to achieve very high resolution using regional mesh refinement. Previous studies using the hydrostatic version of MPAS (H-MPAS) with the physics parameterizations of Community Atmosphere Model version 4 (CAM4) found notable resolution dependent behaviors. This study revisits the resolution sensitivity using the non-hydrostatic version of MPAS (NH-MPAS) with both CAM4 and CAM5 physics. A series of aqua-planet simulations at global quasi-uniform resolutions ranging from 240 km to 30 km and global variable resolution simulations with a regional mesh refinement of 30 km resolution over the tropics are analyzed, with a primary focus on the distinct characteristics of NH-MPAS in simulating precipitation, clouds, and large-scale circulation features compared to H-MPAS-CAM4. The resolution sensitivity of total precipitation and column integrated moisture in NH-MPAS is smaller than that in H-MPAS-CAM4. This contributes importantly to the reduced resolution sensitivity of large-scale circulation features such as the inter-tropical convergence zone and Hadley circulation in NH-MPAS compared to H-MPAS. In addition, NH-MPAS shows almost no resolution sensitivity in the simulated westerly jet, in contrast to the obvious poleward shift in H-MPAS with increasing resolution, which is partly explained by differences in the hyperdiffusion coefficients used in the two models that influence wave activity. With the reduced resolution sensitivity, simulations in the refined region of the NH-MPAS global variable resolution configuration exhibit zonally symmetric features that are more comparable to the quasi-uniform high-resolution simulations than those from H-MPAS that displays zonal asymmetry in simulations inside the refined region. Overall, NH-MPAS with CAM5 physics shows less resolution sensitivity compared to CAM4. These results provide a reference for future studies to further explore the use of NH-MPAS for high-resolution climate simulations in idealized and realistic configurations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Disruption event characterization and forecasting in tokamaks

Disruption prediction and avoidance is a critical need for next-step tokamaks, such as ITER. Disruption Event Characterization and Forecasting (DECAF) research fully automates analysis of tokamak data to determine chains of events that lead to disruptions and to forecast their evolution allowing sufficient time for mitigation or complete avoidance of the disruption. Disruption event chains related to local rotating or global magnetohydrodynamic (MHD) modes and vertical instability are examined with warnings issued for many off-normal physics events, including density limits, plasma dynamics, confinement transitions, and profile variations. Along with Greenwald density limit evaluation, a local radiative island power balance theory is evaluated and compared to the observation of island growth. Automated decomposition and analysis of rotating tearing modes produce physical event chains leading to disruptions. A total MHD state warning model comprised of 15 separate criteria produces a disruption forecast about 180 ms before a standard locked mode detector warning. Single DECAF event analyses have begun on KSTAR, MAST, and NSTX/-U databases with thousands of shot seconds of device operation using from 0.5 to 1 × 10 6 tested sample times per device. An initial multi-device database comparison illustrates a highly important result that plasma disruptivity does not need to increase as βN increases. Global MHD instabilities, such as resistive wall modes (RWMs), can give the briefest time period of warning before disruption compared to other physics events. In an NSTX database with unstable RWMs, the mode onset, loss of boundary and current control, and disruption event warnings are found in all cases and vertical displacement events are found in 91% of cases. An initial time-dependent reduced physics model of kinetic RWM stabilization created to forecast the disruption chain predicts instability 84% of the time for experimentally unstable cases with a relatively low false positive rate. Instances of the disruption event chain analysis illustrate dynamics including H–L back transitions for rotating MHD and global RWM triggering events. Furthermore, disruption warnings are issued with sufficient time before the disruption (on transport timescales) to potentially allow active profile control for disruption avoidance, active mode control, or mitigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Forecasting [C II ] Line-intensity Mapping Measurements between the End of Reionization and the Epoch of Galaxy Assembly

We combine recent simulation work on the SFR–[C II ] correlation at high redshift with empirical modeling of the galaxy–halo connection (via UniverseMachine) to forecast [C ii] auto power spectra from z ~ 4 to z ~ 8. We compare these to sensitivities realistically expected from various instruments expected to come online in the next decade. If the predictions of our model are correct, [C II ] should be detectable up to z ~ 6 in this generation of surveys, but detecting [C II ] past the end of reionization will require a generational leap in line-intensity survey capabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

CICE, The Los Alamos Sea Ice Model

The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea ice forecasting in the polar regions and in numerical weather prediction models.

Hunke, Elizabeth↗
DOE OSTI2026

New framework for benchmarking decadal predictions leveraging the PCMDI Metric Package with interactive visualization

Reliable climate predictions across multiple timescales are increasingly critical as climate-related risks continue to rise. With the growing number and diversity of climate prediction systems, systematic intercomparison has become essential. Here, we present a comprehensive evaluation framework based on the PCMDI Metric Package to assess the performance of multiple decadal climate prediction systems. Unlike uninitialized simulations, initialized predictions exhibit bias and predictive skill that evolve with forecast lead time. To address this, we introduce (1) model-by-lead-time portrait plots, which efficiently summarize metrics of global temperature, precipitation, and Arctic/Antarctic sea-ice extent, and (2) an HTML-based interactive visualization platform that provides detailed regional and seasonal diagnostics of model bias, skill scores, and ensemble spread for each model and lead time. Comparisons with uninitialized simulations further quantify the relative impacts of initialization and external forcing on prediction skill. The proposed framework provides a scalable and transparent approach for multi-model climate prediction assessments and can be readily extended to a wide range of operational and research forecasting systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

CICE, The Los Alamos Sea Ice Model

The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea ice forecasting in the polar regions and in numerical weather prediction models.

Hunke, Elizabeth↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2016

Large Scale GPU Accelerated PPMLR-MHD Simulations for Space Weather Forecast

PPMLR-MHD is a new magnetohydrodynamics (MHD) model used to simulate the interactions of the solar wind with the magnetosphere, which has been proved to be the key element of the space weather cause-and-effect chain process from the Sun to Earth. Compared to existing MHD methods, PPMLR-MHD achieves the advantage of high order spatial accuracy and low numerical dissipation. However, the accuracy comes at a cost. On one hand, this method requires more intensive computation. On the other hand, more boundary data is subject to be transferred during the process of simulation. In this work, we present a parallel hybrid solution of the PPMLR-MHD model implemented using the computing capabilities of both CPUs and GPUs. We demonstrate that our optimized implementation alleviates the data transfer overhead by using GPU Direct technology and can scale up to 151 processes and achieve significant performance gains by distributing the workload among the CPUs and GPUs on Titan at Oak Ridge National Laboratory. The performance results show that our implementation is fast enough to carry out highly accurate MHD simulations in real time.

Computer Science↗
DOE OSTI2023

Raw WRF model output files from a LASSO-CACTI simulation

The raw model output from a Weather Research and Forecasting (WRF) simulation from the Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) deep-convection scenario for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. The LASSO-CACTI simulations span grid spacings from 7.5 km to 100 m through the use of four nested domains, labeled d1 through d4. The simulations are of convection near the Sierras de Córdoba mountain range, roughly centered on the ARM Mobile Facility. More information can be found at https://www.arm.gov/capabilities/modeling/lasso. The raw model output is in netCDF format as output by the WRF model, i.e., the “wrfout” files, with the files renamed for archival purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 t C ha -1 year -1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 t C /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Denitrification and the challenge of scaling microsite knowledge to the globe

Here, our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

59 BASIC BIOLOGICAL SCIENCES↗