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DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The impact of agricultural trade approaches on global economic modeling

Future socioeconomic and climate scenarios have been explored using integrated assessment models (IAMs) to understand interactions between human development and global environmental change in the long run. However, differences in trade modeling approaches are an important source of uncertainty in the assessments, particularly for regional projections. Here, we explore the critical role of trade modeling in assessing the potential future of global agroeconomics and terrestrial carbon emissions with a well-established IAM, the Global Change Assessment Model (GCAM). We update the crop trade modeling framework in GCAM from a Heckscher-Ohlin-Vanek (HOV) structure with integrated world markets (IWM) to a newly developed logit-based Armington approach with segmented regional markets (SRM). The updates make it possible to study the sensitivity of model projections of future agroeconomics and terrestrial carbon emissions to assumptions of the state and magnitude of global market integration. Our results demonstrate that assuming full global market integration, represented by homogeneous product modeling, neglecting economic geography, and excluding margins and tariffs, could lead to lower cropland use (i.e., by 115 million hectares globally) and terrestrial carbon fluxes (i.e., by 25%) by the end of the century. However, the results are highly heterogeneous across regions with more pronounced regional trade responses driven by global market integration. Our study highlights the critical role of trade modeling around product differentiation, economic geography, and regional trade parameterization in global economic or integrated assessment modeling. The results also imply that further reconciliations in trade model approaches could improve the convergence of regional results among models in model intercomparison studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Recent advances in integrated hydrologic models: Integration of new domains

Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, in this study, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗

DOE OSTI2024

Scale-Bridging Optimization Framework for Desalination Integrated Produced Water Networks

In this work, we develop a Pyomo-based non-linear optimization strategy that includes rigorous MVR models. The detailed desalination unit is integrated into the multiperiod produced water network problem using the trust region filter (TRF) method. TRF decomposes the integrated problem into a master problem consisting of the network variables and a simplified surrogate model for the detailed desalination unit. The surrogate is updated using zero and first-order corrections from the optimal solution of the detailed models at every iteration. This framework allows us to co-optimize the design of the desalination units and operating policy for the multiperiod network. A common design is ensured across all periods using global capacity constraints. We validate the solution obtained using the TRF method by solving the full integrated problem for small network instances and show our results on real case studies on produced water networks from the Permian and Appalachian basins. In this work, we describe our TRF formulation, give details on our implementation in Pyomo, and analyze the results obtained by solving the optimization problem using IPOPT. We also present a discussion on the computational efficiency and scaling using the TRF approach against a full-scale integration of the rigorous models within the water network.

Naik, Sakshi↗

DOE OSTI2024

Machine learning-enhanced model-based scenario optimization for DIII-D

Abstract Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

data visualization↗

DOE OSTI2021

Robust Distributed State Estimator for Interconnected Transmission and Distribution Networks (Final Report RPPR-1)

This project’s objective is to develop a combined transmission and distribution state estimator which accounts for very large system size and model complexity (by way of distributing the computations) and large number of solar PV units connected to the distribution system on multiple feeders. The project not only provides a robust formulation and solution to this problem but also tests the solution by implementing it on a well-established large utility system. It introduces several improvements with respect to the state of the art in existing state estimation software: (a) The developed state estimator (SE) allows robust and accurate monitoring of bidirectional flows in distribution systems which result due to the distributed energy sources which are not observable and thus not incorporated in generation dispatch; (b) Large utility systems with tens of thousands of transmission buses and hundreds of thousands of distribution nodes are difficult to model as a single integrated system. This shortcoming is addressed by developing a “scalable distributed computational framework” which allows splitting the ultra large system models into several small subsystems and coordinating their solution by a robust and practical state estimation formulation; (c) Measurement errors irrespective of their locations are detected and removed by the developed state estimator. Historically, transmission and distribution systems were analyzed and operated as two independent systems. Given the non-transposed short feeder sections, unevenly loaded phases, strictly radial configuration and unidirectional power flows in the absence of remote generation, distribution system analysis was customized to account for these characteristics. However, some of these assumptions are no longer valid (non-radial configuration, bidirectional power flows) and thus distribution system analysis should be revisited. Furthermore, in the past, the interaction between the transmission and distribution systems was quite passive, where distribution substations were modeled as lumped loads in the transmission system model. With substantial generation injected by renewable generation located in the distribution systems, such modeling will no longer be accurate. The developed state estimator facilitates proper monitoring of the interactions between the transmission and distribution systems and enables smart dispatch of these units which are made observable by the state estimator.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs

Coarse-Grained Reconfigurable Arrays (CGRAs) can achieve higher energy-efficiency than general-purpose processors and accelerators or fine-grained reconfigurable devices, while maintaining adaptability to different computational patterns. CGRAs have shown some success as a platform to accelerate machine learning (ML) thanks to their flexibility, which allows them to support new models not considered by fixed accelerators. However, current solutions for CGRAs employ low level instruction-based compiler approaches and lack specialized compilation infrastructures from high-level ML frameworks that could leverage semantic information from the models, limiting the ability to efficiently map them on the recon- figurable substrate. This paper proposes ML-CGRA, an integrated compilation framework based on the MLIR infrastructure that en- ables efficient ML acceleration on CGRAs. ML-CGRA provides an end-to-end solution for mapping ML models on CGRAs that out- performs conventional approaches by 3.15× and 6.02 × on 4×4 and 8×8 CGRAs, respectively.

machine learning↗

DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

NASA NTRS1980

Life near the Roche limit - Behavior of ejecta from satellites close to planets

A study of the dynamics of nearby debris from impact craters was made to explain the distinctive features seen on Phobos, Deimis, and Amalthea. The planetary tides and satellite rotation were considered, and the usual pseudo-energy (Jacobi) integral was numerically calculated in the framework of a restricted body problem where satellites are modelled as triaxial ellipsoids rather than point masses. Iso-contours of this integral show that Deimos and Amalthea are entirely closed by Roche lobes, and the surfaces of their model ellipsoids lie nearly along equipotentials. Presently, the surface of Phobos overflows its Roche lobe, except for regions within a few km of the sub-Mars and anti-Mars points. The behavior of crater ejecta from the satellites of Mars were also examined by numerical integration of trajectories for particles leaving their surfaces in the equatorial plane.

Dobrovolskis, A. R.↗

NASA NTRSDate not supplied

Precision Landing Performance of a Human-Scale Lunar Lander Using a Generalized Simulation Framework

NASA has established goals of returning humans to Moon with an initial landing by 2024 and a subsequent sustained presence by 2028, which will require technological advances in spacecraft navigation to enable precision landing. The ability to assess the navigation performance of these new and existing technologies is critical to identifying areas of risk reduction and investment. To that end, the Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project has demonstrated that a detailed six degree-of-freedom integrated performance simulation framework can provide information on and assessment of expected navigation performance. This framework incorporates engineering models of the on-board spacecraft guidance, navigation, and control systems at varying levels of fidelity. Recent advances in the development of this integrated performance simulation permit running these systems “in-the-loop,” rather than assuming perfect knowledge of the spacecraft states. This development, coupled with fast simulation time and modularization of the various system models, enables a wide variety of system trades to be assessed at once. This paper presents a summary of the advances in the SPLICE simulation framework, updates to the spacecraft navigation models, and an application of the framework to characterize the precision landing performance of a human-scale lunar lander. A series of trade studies examining effects of ground state update qualities shows that given all other assumptions, sufficiently accurate Deep Space Network (DSN) measurements can enable safe and precise human-scale Lunar landings.

Spacecraft navigation↗

NASA NTRS2022

Precision Landing Performance and Technology Assessments of a Human-Scale Lunar Lander Using a Generalized Simulation Framework

"D'Souza, Sarah", 'Pensado, Alegandro R.↗

DOE OSTI2023

MDDC Multi-Length Scale Data Architecture Contribution Report – PNNL, INL, ANL, LANL and ORNL

This report offers a comprehensive view of data streams currently generated at Pacific Northwest National Laboratory, Idaho National Laboratory, Argonne National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory set to integrate into the evolving Multi-Dimensional Data Correlation framework at Oak Ridge National Laboratory. Developed by the Advanced Materials and Manufacturing Technologies program, the Multi-Dimensional Data Correlation framework serves as a cutting-edge software to manage data relevant to advanced manufacturing and material behavior in advanced reactors. The report defines data streams, highlights their generation methods and visualization methods both for experimental and computational aspects relevant to the Advanced Materials and Manufacturing Technologies project. A logical next step for this work is to integrate the MDDC framework into PNNL’s, INL’s, ANL’s, LANL’s and ORNL’s fabrication, experimentation, and modelling workflows. This would require setting up the MDDC framework at PNNL, INL, ANL, and LANL and integrating it into the data collection and storage for these different activities.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

DOE OSTI2025

An open-source hybrid unstructured mesh - CAD fusion multiphysics analysis workflow in SALAMANDER

Plasma facing components in fusion devices will endure extreme neutron and heat fluxes. To facilitate their design using simulation tools, the open-source Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX) framework is being developed to model these components with a high-fidelity multi-physics multi-dimensional approach. It can iteratively resolve couplings between all the physics at play, from neutron radiation, to thermomechanics, to near-wall plasma dynamics. This framework is based on the Multiphysics Object Oriented Simulation Environment (MOOSE), which is developed by a collaboration of US National Laboratories since 2008, for advanced nuclear, geomechanics simulations and other applications. FENIX couples numerous simulation tools, including OpenMC, the Tritium Migration Analysis Program v8, the NekRS CFD software, and most MOOSE modules. For the coupling of radiation transport and other physics, FENIX supports a hybrid workflow between Computer Assisted Design (CAD) and unstructured mesh geometries. The CAD can be generated from skinning the unstructured mesh, to enable a coarse geometry for efficient particle transport, but still resolving the local material compositions and temperature gradients. Neutron transport is performed using DAGMC on the CAD, and Cardinal, integrated in FENIX, maps tallied quantities, such as the heat deposition or the tritium generation rates, from a tally volumetric mesh to the other physics’ unstructured mesh. This coupling was exercised on a simplified tokamak geometry, coupling neutron transport with the heat conduction equation, and on a monoblock divertor problem, coupling additionally with tritium migration. Mesh convergence studies highlight the importance of the mapping conservativeness. Coupling with thermo-mechanics is further enabled by the generalization of the approach to moving meshes. The presentation will include these coupled analysis as well as an update on status of the FENIX framework.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2008

Intelligent Elements for ISHM

There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors.

Schmalzel, John L.↗