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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or time- intensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)—a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building’s monthly energy usage in response to weather conditions. The tool benchmarks a building’s electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE’s Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization’s challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

Li, han↗
DOE OSTI2024

Techno-economic analysis of bioplastic and biofuel production from a high-ash microalgae biofilm cultivated in effluent from a municipal anaerobic digester

Rotating Algae Biofilm Reactors (RABRs) are a promising technology for efficient treatment of wastewater and production of algae-based bioproducts. However, RABR-grown algae can contain a high content of ash (30–60 wt%, dry basis), which influences the technical and economic feasibility of bioproduct conversion processes. In this report, experimental studies and economic analysis were conducted to compare different processes for bioproduct conversion of a high-ash microalgae biofilm grown using a RABR treating 0.6 million gallons per day of anaerobic digestion centrate at the Central Valley Water Reclamation Facility in Salt Lake City, UT. Process and economic models were developed and compared for three conversion processes: 1) the production of bioplastics, 2) the production of bioplastics with a lipid-extraction pretreatment, and 3) the production of biocrude via hydrothermal liquefaction. Techno-economic analysis was performed for each conversion process, including three cases for algae productivity: 231, 391, and 577 metric tons per year (dry basis). The calculated value for the minimum plastic selling price (MPSP) of bioplastics produced from algae ranges from $\$4050$ to $\$3520$ per metric ton based on the baseline and final productivity cases of the RABR, respectively. The extraction of lipids in addition to bioplastic production results in an MPSP of $\$4570$ to $\$4000$ per metric ton for the same productivity cases. The relatively small production scale and complex processing for hydrothermal liquefaction results in a minimum fuel selling price of the biocrude of $\$5.32$ per gallon of gasoline equivalent. In conclusion, the conversion process for bioplastic production from whole algae has the highest income:expense ratio and the most cost-competitive pricing of the three modeled processes.

09 BIOMASS FUELS↗
DOE OSTI2021

A Technical and Economic Assessment of LWR Flexible Operation for Generation and Demand Balancing to Optimize Plant Revenue

With increased penetration of subsidized variable renewable energy (VRE) resources and competition from low natural gas prices, existing light water reactor (LWR) nuclear power plants (NPPs) are struggling to remain economically competitive. This work examines the potential economic competitiveness of various thermal energy storage (TES) technologies when coupled directly or indirectly with a NPP. To highlight their relative economic competitiveness, we contrast several energy storage solutions in stochastic dispatch optimization. We leverage data from recent work analyzing a range of TES technologies with varying capital costs, performance, and technology readiness level (TRL) to establish our case. We explore inserting these technologies into an electricity market with existing nuclear generation and large projected variable renewable energy (VRE) penetration. Although these technologies' projected capital costs may make them unlikely candidates in their current state, this analysis demonstrates a high-fidelity techno-economic analysis of energy storage. Furthermore, as the projected cost of energy storage technologies evolves, this analysis sets a precedent for similar future investigations. One region with projected trends that may be unfavorable for existing nuclear capacity is the New York Independent System Operator (NYISO) market. New York state’s baseload generation has been historically provided by fossil-fired and nuclear assets. However, amid economic pressures from subsidized VREs and low natural gas prices, the state has recently deactivated Indian Point nuclear power plant units 2 and 3. Furthermore, the state plans to meet its zero-emission generation target by 2040 by replacing fossil-fired capacity with significant investments in VRE resources like wind and solar photovoltaic (PV) and battery storage. Increased intermittent resource penetration lowers the baseload power requirement, adding further economic pressure to the state’s three remaining NPPs still in operation. With three NPPs still in operation in New York, this work analyzes potential economic benefits to NPPs on the New York grid when directly or indirectly coupled with various TES technologies. This work requires two modeling steps to analyze the potential economic benefits of various system configurations of the TES directly or indirectly coupled with nuclear. First, this analysis leverages capacity expansion modeling by experts at the Electric Power Research Institute (EPRI). Using their deterministic capacity expansion model, U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN), EPRI analysts evaluated the capacity and generation evolution of the New York state energy market under four projection scenarios. These four projection scenarios were developed to represent the potential evolution of the capacity and generation in NYISO from 2015 to 2050 under various economic, technology, and policy constraints. The results from these capacity expansion models are then used as boundary conditions in the second modeling step. The second modeling step uses the Holistic Energy Resource Optimization Network (HERON) for a set of stochastic techno-economic analyses (STEAs) to investigate the potential increase in the economic viability of various configurations of the TES. With no current capacity expansion capabilities, HERON takes the data generated from US-REGEN for 2050 to generate synthetic load, solar, and wind data. Then HERON economically optimizes the capacity and dispatch of the various TES configurations. The potential economic benefit is the differential net present value (NPV) of the TES configurations from the no-TES baseline. As a stochastic techno-economic analysis package, HERON introduces uncertainty into the economic metrics, while US-REGEN trades resolution for reduced computational complexity. Using HERON also allows the modeling of direct thermal coupling, a feature not common in capacity and dispatch models. As expected, with high capital costs, the costs of introducing energy storage for all the technologies considered outweighed the potential economic benefit of this strategy for flexible plant operation. The benefit of this analysis is primarily in demonstrating a workflow that examines innovative solutions to increase NPP revenue via TES coupling. HERON’s stochastic capacity and dispatch optimization process used in this work has proven an effective tool in observing and evaluating the impact of introducing storage technologies in a grid energy system.

25 ENERGY STORAGE↗
DOE OSTI2021

Biomass supply chain equipment for renewable fuels production: A review

The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and adequation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.

: Feedstock, Collection and Adequation, Renewable ↗
DOE OSTI2024

Scalable Implicit Solvers with Dynamic Mesh Adaptation for a Relativistic Drift-Kinetic Fokker–Planck–Boltzmann Model

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker–Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. Here, we develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dynamic mesh adaptivity. A new data management framework in the PETSc library based on the p4est library is developed to enable simulations with dynamic adaptive mesh refinement (AMR), distributed memory parallelization, and dynamic load balancing of computational work. This framework and the runaway electron solver building on the framework are able to dynamically capture both bulk Maxwellian at the low-energy region and a runaway tail at the high-energy region. To effectively capture features via the AMR algorithm, a new AMR indicator prediction strategy is proposed that is performed alongside the implicit time evolution of the solution. This strategy is complemented by the introduction of computationally cheap feature-based AMR indicators that are analyzed theoretically. Numerical results quantify the advantages of the prediction strategy in better capturing features compared with nonpredictive strategies; and we demonstrate trade-offs regarding computational costs. The robustness with respect to model parameters, algorithmic scalability, and parallel scalability are demonstrated through several benchmark problems including manufactured solutions and solutions of different physics models. We focus on demonstrating the advantages of using implicit time stepping and AMR for runaway electron simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Current state and future projections of drying processes in the US food and pulp and paper sectors: Energy, economic, and environmental assessment

The pulp and paper (P/P) and food sectors are the third- and fifth-largest industrial energy consumers in the United States, with total on-site energy consumption of 2,039 TBtu and 1,144 TBtu, respectively. Thermal drying processes for moisture removal, which are energy-intensive, play a critical role in both industries. This study is the first to evaluate state- and national-level US drying energy demand for these sectors from 2020 to 2050. To complete this evaluation, we developed a thermodynamic modeling framework integrated with economic and environmental models to compute product-specific drying energy intensity and estimate the sector-specific costs and emissions profiles associated with drying operations. The model-predicted energy intensity was validated against the literature. Using current and projected annual production volumes in these sectors, we estimated total drying energy use. Results indicate that drying accounts for 22 % of total energy consumption in the P/P sector and 10 % in the food sector. The estimated annual energy cost (2020) to operate thermal dryers is $\$$919 M in the P/P sector and $\$$417 M in the food sector. Additionally, drying contributes to 25 % of total CO 2 e emissions in the P/P sector (including biogenic) and 15 % of emissions in the food sector. Regional performance shows that the Southern US is the leading energy consumer for P/P drying, whereas the Midwest leads in food drying. This study presents both potential solutions to enhance drying efficiency and barriers to implementation. Energy efficiency improvements, low-carbon fuels, and electrification are discussed as key pathways for reducing costs and optimizing industrial drying processes.

3E analysis↗
DOE OSTI2022

The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake

In this paper, a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at a low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics. This is achieved by creating so-called observation points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computationally lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to Simulator for Offshore Wind Farm Applications (SOWFA) simulations in three- and nine-turbine cases under static and changing environmental conditions. The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three-turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model-based dynamic wind farm control.

17 WIND ENERGY↗
DOE OSTI2024

The Grid Value of Ocean Current Energy in Florida

Ocean current energy technology has been proposed as a potential contributor to Florida's energy portfolio. There has been limited investigation of how this energy would be valued when integrated into the Florida electrical grid. This study assesses three future grid scenarios to evaluate the impact of adding zero-cost ocean current energy to each. The Resource Planning Model, a tool developed by the National Renewable Energy Laboratory, is used to identify the least-cost generation mix through 2050, with and without ocean current energy. The first scenario is a base case and assumes existing policies in which the addition of ocean current energy does not retire fossil-based technologies but variable generation technologies. In the second scenario, solar and storage technologies are lower cost, and the addition of ocean current generation enables those technologies along with wind to retire existing natural gas units earlier. In the third scenario, which requires a 95% reduction in carbon emissions from 2020 levels by 2050, ocean current energy can play a role in decarbonization along with other variable generation technologies. This analysis is intended to inform stakeholders on the opportunity, potential challenges, and overall value to the grid of ocean current technology from a reliability and availability focused perspective.

capacity expansion model↗
DOE OSTI2020

Joint Planning of EV Fast Charging Stations and Power Distribution Systems With Balanced Traffic Flow Assignment

To tackle the challenges introduced by the fast-growing charging demand of electric vehicles (EVs), the power distribution systems (PDSs) and fast charging stations (FCSs) of EVs should be planned and operated in a more coordinated fashion. However, existing planning approaches generally aim to minimize investment costs in PDSs while ignoring the risk of worsening traffic conditions. To overcome this research gap, this article integrates the interests of traffic networks into PDS and FCS joint planning model to mitigate negative impacts on traffic conditions caused by installing FCSs. First, a novel microscopic method that is different from traditional assignment methods is proposed to simulate the influences of FCSs on traffic flows and EV charging loads. Then, a multiobjective joint planning model is developed to minimize both the planning costs and unbalanced traffic flows. A new bilayer Benders decomposition algorithm is designed to solve the proposed joint planning model. Numerical results on two practical systems in China validate the feasibility of our microscopic method by comparing the simulated results with real data. Compared with existing approaches, it is also demonstrated that the proposed joint planning approach helps to balance traffic flow assignments and relieve traffic congestion.

bilayer expanded Benders decomposition↗
DOE OSTI2025

Advancing Pumped Thermal Energy Storage Performance and Cost Using Silica Storage Media

Pumped Thermal Energy Storage (PTES) is an electricity storage system that is suitable for long-duration energy storage (10-1000 h) due to its low marginal cost of energy capacity. We present a techno-economic model of a PTES system that uses particle thermal energy storage. Particles have low costs and can be operated over a wide temperature range leading to increased efficiency and reduced costs compared to other PTES designs. We show how the round-trip efficiency, specific power output, capital cost, and levelized cost of storage (LCOS) depend on parameters such as the pressure ratio, heat exchanger approach temperature and pressure loss, and maximum temperature. We compare particle-PTES (P-PTES) performance to PTES which uses liquid thermal energy storage - i.e. molten salt hot storage and methanol cold storage (MS-PTES). We find that using silica particles for storage advances PTES technology: P-PTES can be operated at higher maximum temperatures than MS-PTES. Furthermore, P-PTES can achieve lower approach temperatures in the heat exchangers than MS-PTES without increasing capital costs, because P-PTES uses direct-contact heat transfer in fluidized bed heat exchangers. As a result, we find that P-PTES systems achieve higher round-trip efficiency than MS-PTES (66 % versus 57 %) and lower LCOS (e.g. 0.115 +- 0.03 $/kWhe versus 0.171 +- 0.04 $/kWhe for 10 h discharge). The low cost of particles and containment means that P-PTES can provide long-duration energy storage at low capital cost per unit energy capacity. For example, the total capital cost per unit energy reduces from 245 $/kWhe at 10 h to 38 $/kWhe at 100 h. These costs are considerably lower than MS-PTES (72 $/kWhe at 100 h) and also outcompete current and future Li-ion battery system projections (100-265 $/kWhe).

25 ENERGY STORAGE↗
DOE OSTI2025

Accelerating Combustion and Surface Chemistry Simulations

Design of modern combustion systems relies on computer models to predict how changes in design will affect performance. These models have largely displaced previous methods that rely on the designer’s intuition or costly and time-consuming physical testing. By using improved models, design cycles can be shortened, and cleaner and more efficient combustion devices can be created. This project aims to improve computer simulations of transportation fuels with the goal of making these simulations faster and more accurate for predicting combustion in vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Electrolyzers in the System Advisor Model (SAM): A Techno-Economic Potential Study

A technoeconomic analysis of grid to low temperature electrolysis (Grid-LTE), photovoltaic to low temperature electrolysis (PV-LTE), concentrating solar power to high temperature electrolysis (CSP-HTSE) and a concentrating solar power with PV to high temperature electrolysis (CSP-PV-HTSE) centralized hydrogen production systems are analyzed to assess the economics of system and to provide a baseline for comparing these technologies against hydrogen production cost targets. A framework integrating the system advisor model (SAM) and US Department of Energy hydrogen production models (H2A) is developed to assess these systems. The hydrogen levelized cost given current and future assumptions for technology cost and performance is evaluated at optimal system configurations. The framework described in this report integrates SAM with H2A electrolyzer technologies and provides analysts a detailed technoeconomic method to analyze concentrating and photovoltaic solar technologies to produce energy that are directly coupled to LTE and HTSEs that use that energy to split water into hydrogen and oxygen. The baseline hydrogen levelized cost (HLCs) for the GRID-LTE, PV-LTE, CSP-HTSE, and CSP-PV-HTSE systems in Daggett, CA are 2.82, 3.86, 3.68, and 2.90 $\$$USD 2016/kg H2 and 2.50, 2.13, 2.84, 2.15 $\$$USD 2016/kg H2 in the 2020 and 2050 scenarios respectively. To achieve the $\$$2/kg H2 target in locations with excellent solar resources, cost parameters values aligned with aggressive R&D targets will need to be achieved for all the systems configurations. In Daggett, PV costs of $\$$0.68 /Wac or moderate ATB PV CAPEX projections result in HLCs of $\$$2 /kg H2. Similarly for PV-MSALT-HTSE systems, $\$$0.60 /Wac result in $\$$2/kg H2. For the MSALT-HTSE systems, better than aggressive 2050 ATB salt tower CAPEX projections would be needed to reach $\$$2/kg H2. Molten salt tower capital costs of $\$$2400/kW would enable $\$$2/kg H2 in 2050.

08 HYDROGEN↗
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2020

A Model for Evaluating the Configuration and Dispatch of PV Plus Battery Power Plants

An open-source model was developed to optimize energy storage operation for photovoltaic- (PV-) plus-battery systems with AC-coupled and DC-coupled configurations. It includes the ability to use forecast energy prices to optimize battery charge and discharge on a rolling time horizon. The model allows for exploration of different configurations, including capital costs, inverter performance, dispatch flexibility, and capturing otherwise clipped energy for the DC-coupled system. The model can run 20 full years of hourly data in approximately two seconds, allowing comparison of a large number of configurations. We applied the model in a test case demonstrating reduced inverter clipping for DC-coupled systems and yielded slightly higher overall value than AC-coupled systems, with an approximately 2 percent increase in internal rate of return or benefit/cost ratio. Our results show that at current estimated prices for lithium-ion battery systems, large-scale PV-plus-battery plants are economically viable under the right conditions, with the configuration playing a role in system flexibility and performance. This model provides the ability for project developers, industry professionals, and researchers to use readily available software to quickly evaluate and design these systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗