Modeling Framework For Systems Research Articles

Advanced discovery mechanisms in model repositories

SummaryAs model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository.

Chemical Organization Theory as a General Modeling Framework for Self-Sustaining Systems

This paper summarizes and reviews Chemical Organization Theory (COT), a formalism for the analysis of complex, self-organizing systems across multiple disciplines. Its elements are resources and reactions. A reaction maps a set of resources onto another set, thus representing an elementary process that transforms resources into new resources. Reaction networks self-organize into invariant subnetworks, called ‘organizations’, which are attractors of their dynamics. These are characterized by closure (no new resources are added) and self-maintenance (no existing resources are lost). Thus, they provide a simple model of autopoiesis: the organization persistently recreates its own components. The resilience of organizations in the face of perturbations depends on properties such as the size of their basin of attraction and the redundancy of their reaction pathways. Application domains of COT include the origin of life, systems biology, cognition, ecology, Gaia theory, sustainability, consciousness, and social systems.

Fuzzy Modeling Framework Using Sector Non-Linearity Techniques for Fixed-Wing Aircrafts

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.

Aboveground hydrogen storage – Assessment of the potential market relevance in a Carbon-Neutral European energy system

Hydrogen storage is expected to play a crucial role in the comprehensive defossilization of energy systems. In this context, the focus is typically on underground hydrogen storage (e.g., in salt caverns). However, aboveground storage, which is independent of geological conditions and might offer other technical advantages, could provide systemic benefits and, thereby, gain shares in the hydrogen storage market. Against this background, this paper examines the market relevance of aboveground compared to underground hydrogen storage. Using the open-source energy system model and optimization framework of Europe, PyPSA-Eur, the influence of geological independence, storage cost relations, and technical storage characteristics (i.e., efficiencies) on mentioned market relevance of aboveground hydrogen storage are investigated. Further, the expectable market relevance based on current cost projections for the future is assessed. The studies show that in terms of hydrogen capacities, aboveground hydrogen storage plays a considerably smaller role compared to underground hydrogen storage. Even when assuming comparatively low aboveground storage cost, it will not exceed 1.7% (1.9TWhH2,LHV) of total hydrogen storage capacities in a cost-optimal European energy system. Regarding the amounts of annually stored hydrogen, aboveground storage could play a larger role, reaching a maximum share of 32.5% (168TWhH2,LHVa-1) of total stored hydrogen throughout Europe. However, these shares are only achievable for low cost storage in particularly suited energy system supply configurations. For higher aboveground storage costs or lower efficiencies, shares drop below 10% sharply. The analysis identifies some especially influential factors for achieving higher market relevance. Besides storage costs, the demand-orientation of a particular aboveground storage system (e.g., hydrogen storage at demand pressure levels) plays an essential role in market relevance. Further, overall efficiency can be a beneficial factor. Still, current projections of future techno-economic characteristics show that aboveground hydrogen storage is too expensive or too inefficient compared to underground storage. Therefore, to achieve notable market relevance, rather drastic cost reductions beyond current expectations would be needed for all assessed aboveground hydrogen storage technologies.

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

Sparse Identification of Fractional Chaotic Systems based on the time-domain data

The complexity inherent in defining and computing fractional derivatives renders the development of a data-driven modeling framework for fractional-order systems more formidable than for their integer-order counterparts. This study introduces a methodology for sparsely identifying fractional chaotic systems by leveraging time-domain data to ascertain the system’s governing equations. Initially, we establish a sparse identification framework specifically tailored for fractional order systems and introduce a joint iterative thresholding method designed to identify these systems’ fractional and integer order components concurrently. Moreover, this study conducts a comparative analysis of two error criteria for model selection. It introduces a strategy predicated on minimizing the distance to balance sparsity and model fitting error optimally. To validate the efficacy and precision of the proposed methodology, numerical simulations were conducted on fractional order simplified Lorenz and Chua’s circuits, affirming the robustness and accuracy of our approach.

SYSTEMIC PERSPECTIVE OF TERM RISK IN BANK FUNDING MARKETS

The transition from term-based reference rates to overnight reference rates has created a dislocation in the market-making processes between the interbank and non-interbank funding, and their respective derivatives markets. This dislocation can be attributed to differences in funding and corresponding interest rate swap transactions, a thesis we explain and characterize in detail. It is then shown how this dislocation may be resolved. Based on a systemic perspective of a stylized financial system, an aggregated banking system is constructed that is void of idiosyncratic credit risks but still vulnerable to liquidity risks. Within this setup, a mathematical modeling framework for term-cognizant interest rate systems is derived that enables the pricing and valuation of bank term funding and associated derivatives transactions with varying liquidity characteristics. Other outcomes include: (i) a detailed analysis of the incomplete market paradigm that encapsulates bank term funding rates and the risk management processes involved therein; and (ii) a recovery of consistency in the pricing and valuation between funding and related interest rate swap transactions, along with a mechanism to exchange term risk.

Flight-Path Optimization for a Hybrid-Electric Aircraft

Abstract This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles.

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders.

SystemC framework for architecture modelling of electronic systems in future particle detectors

The prototyping cost in advanced technology nodes and the complexity of future detectors require the adoption of a system design approach common in the industry: design space exploration through high-level architectural studies to achieve clear and optimized specifications.This contribution proposes a configurable SystemC framework to simulate the readout chain from the front-end chips to the detector back-end. The model is transaction accurate, includes an event generator, interfaces with real physics events, and provides metrics such as readout efficiency, latency, and average queue occupancy.This contribution details the structure of the framework and describes a case study based on the LHCb VeLo upgrade II.

Thermo-Electrochemical Simulation of Large-Format Li-Ion Cells in 3D Using the Battery Modelling Toolbox (BattMo)

Computer-based modelling and simulation can be a valuable tool when developing electrochemical devices: it offers possibilities for insight into internal properties not available in experiments, it can guide the development and testing of new materials in the device, it can provide time-efficient alternatives for optimizing operating procedures, and more. In this contribution we will give an overview of the Battery Modelling Toolbox (BattMo), which is an open-source framework for continuum modelling of electrochemical-thermal systems. It has been primarily developed for the modelling and simulation of Li-ion battery cells using pseudo X-dimensional (PXD, X=2,3,4) Doyle-Fuller-Newman models, but has also successfully been applied to electrolyzers. BattMo is developed in MATLAB and has a high-performance Julia version (BattMo.jl) in development.The fundamental equations of mass, charge and energy conservation are in BattMo discretized using a low-order implicit finite volume method. These are suitable methods, since they are conservative (meaning that mass, charge, etc., are conserved), and guarantee monotone solutions (meaning that the solutions will be positive if the data is positive). These properties provide a simulation foundation that is robust and stable also on complex 3D grids -- essential features when modelling real-world systems. As with most numerical methods, the most time-consuming part is to solve the resulting linear system of equations. In BattMo we make use of state-of-the-art block-wise multigrid methods to obtain high performance.Working with BattMo may be done using a GUI, which generates a json file which completely specifies a simulation. This json file can be used directly as input to BattMo via the MATLAB/Julia prompt, or used in cloud resources. It is also possible to use a json file as a base configuration and modify the parameters manually or programmatically, for example to investigate a specific system response when changing some parameters of interest. BattMo also provides functionality for automatic gradient-based optimization and parameter estimation that can be used for maximizing or minimizing some goal quantity, and matching to experimental data. A key component for performing efficient gradient-based optimization and parameter estimation in the case of many parameters (for example when having one parameter value in each grid cell) is the use of so-called adjoints. Using adjoints allow for the gradients in the sensitivity analysis to be computed with a constant cost that is independent on the number of parameters, thus avoiding the curse of dimensionality.Much attention is spent on making the BattMo json input compatible with other simulation frameworks such as PyBamm, COMSOL and CideMod. This is to accelerate research and development by taking advantage of the strengths of different packages, as well as providing not only reproducible but also reliable science. BattMo is developed alongside BattINFO, which is a battery interface ontology based on EMMO (Elementary Multiperspective Material Ontology) that is developed in the BIG-MAP EU H2020 project. We also seek to support the BPX standard developed by the PyBamm team.In this talk we will overview the basic features of BattMo and see how the modular approach used in the software simplifies development of new models. BattMo contains many examples, and we will here present thermal simulation of 1D and 3D cells with LFP and LNMO battery chemistries in pouch cell format and investigate the effects of the tabless design in the 4680 cylindrical cell format (see the Figure below).BattMo is available at https://github.com/BattMoTeam and is developed with the support from the EU H2020 innovation programs under the grants Hydra (875527) and BIG-MAP (957189). Figure 1

Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response

Chlor-alkali electrolysis plays a significant role in Germany's electricity demand, with a share of >2%. It offers a promising avenue for leveraging demand response strategies. In times of escalating electricity prices, load shifting can help to maintain economic competitiveness of domestic industries. This study aims to assess the potential for electricity cost savings and related CO2 emission reductions through optimal load shifting practices for chlor-alkali electrolysis, considering both current and projected future electricity prices. The research employs the mixed-integer energy system modeling framework IRPopt to model and optimize a chlorine value chain under various scenarios. A sensitivity analysis is conducted to identify the primary factors driving electricity cost savings. The findings reveal that, based on the 2019 electricity price distribution (before pandemic and energy crisis), load shifting can yield electricity cost savings of 5.8% and CO2 emission reductions of 2.7%. As the share of variable renewable energy sources increases along with other driving factors, the electricity price spread is projected to rise from 28% in 2019 to an average of 87% in 2040 scenarios. Consequently, electricity cost savings rise up to 22% and CO2 emission reductions up to 10%. The primary drivers behind electricity cost savings include electricity price spreads and the utilization and operating range of the electrolyzer. In addition to the business-level advantages, the resulting decrease in residual load and lower CO2 emissions contribute to the integration of variable renewable energy sources and the achievement of decarbonization targets at the economic level.

Ramping up the hydrogen sector: An energy system modeling framework

With the transition towards a decarbonized society, energy system integration is becoming ever more essential. In this transition, the energy vector hydrogen is expected to play a key role as it can be produced from (renewable) power and natural gas with carbon capture and storage and utilized in a plethora of applications and processes across sectors. Despite global hydrogen demand reaching 94 million tonnes in 2021, still less than 0.7% of it is supplied by low-emission hydrogen. Moreover, to date hydrogen production is mostly located in close proximity to where it is used. In order to link future production and demand sites, it is planned to re-purpose existing natural gas and expand dedicated hydrogen pipelines. During the early stages of ramping up the hydrogen sector (2020s and early 2030s), however, blending natural gas with hydrogen for joint pipeline transmission has been suggested. Against this background, this paper studies hydrogen blending from a modeling perspective, both in terms of the implications of considering (or omitting) technical modeling details and in terms of the potential impact on the ramp-up of the hydrogen sector. To this end, we present a highly modular and flexible integrated sector-coupled energy system optimization model of the power, natural gas, and hydrogen sectors with a novel gas flow formulation for modeling blending in the context of steady-state gas flows. A stylized case study illustrates that hydrogen blending has the potential to initiate and to facilitate the ramp-up of the hydrogen sector under certain assumptions, while omitting the technical realities of gas flows – particularly in the context of blending – can result in suboptimal expansion planning not only in the hydrogen, but also in the power sector, as well as in an operationally infeasible system.

Computationally efficient neural hybrid automaton framework for learning complex dynamics

This paper proposes a computationally efficient and effective data-driven modeling framework for dynamical systems. The proposed modeling framework employs a collection of shallow neural networks known as Extreme Learning Machines (ELMs) to model local system behaviors along with data-driven inferred transitions among local models to establish a neural hybrid automaton model. First, the sampled system inputs are mapped to the corresponding feature spaces to obtain data-driven partitions, which subsequently define the transitions and invariants of the neural hybrid automaton model through a novel data-driven mode clustering process. Then, a collection of ELMs are trained to approximate the local dynamics. The learning processes integrate a segmented data merging procedure for location identification and a local dynamics modeling process. The proposed neural hybrid automaton models can capture behaviors of complex dynamical systems with high modeling precision but significantly lower computational complexities in computationally expensive tasks such as training and verification, which are traditionally considered to be computationally expensive tasks for neural network models. A computationally efficient set-valued reachability analysis method which is commonly used in safety verification is then developed based on interval analysis and a novel Split and Combine process. Finally, applications to modeling the limit cycle and human handwritten motions are presented to show the effectiveness and efficiency of our approach.

Surrogate modeling of parameterized multi-dimensional premixed combustion with physics-informed neural networks for rapid exploration of design space

Parametric optimization is a critical component in designing and prototyping combustion systems. However, existing parametric optimization methods often suffer from either insufficient prediction accuracy or computationally prohibitive data generation costs. This study develops and validates an efficient and robust field-resolving surrogate modeling framework for combustion systems based on physics-informed neural networks (PINNs). This study comprises three progressive steps. (1) We propose a PINNs framework for premixed combustion and validate its accuracy with direct numerical simulation (DNS) for one-dimensional and two-dimensional canonical premixed combustion problems. Results show a robust predictive capability of PINNs with R2 generally above 0.99. (2) We continue to develop a field-resolving surrogate modeling framework for parameterized two-dimensional methane-air jet combustion system with five input parameters: inlet equivalence ratio, temperature, velocity, diameter, and coflow temperature. The PINNS surrogate model after unsupervised training is used to predict the combustion fields for 32 input parameter combinations, and the results are compared against corresponding numerical simulations. The surrogate model accurately predicts the temperature fields with R2 over 0.95, except in case of flashback, where the accuracy is slightly lower. Good consistency between PINNs and numerical simulations is also demonstrated for four combustion performance metrics: flame length, maximum temperature, outlet temperature, and outlet CO2 mass fraction. (3) Based on the validated field-resolving PINNs surrogate model, sensitivity analysis of the parameterized combustion system is performed, and its implications for engineering applications are discussed. Most notably, distinct from common data-driven and machine learning approaches, the proposed PINNs models are entirely physics-driven and do not use any training datasets, saving tremendous amount of time in the design of experiments (DOE) and case-by-case simulations. Overall, the results of this study demonstrate PINNs can be an efficient and robust surrogate modeling approach for parameterized combustion system visualization, design, optimization, and active control.

System dynamic modelling to assess the influential factors affecting roughing filter and slow sand filter performance in treating culinary wastewater

This research aimed to determine the factors that influence the performance of a slow sand filter (SSF) equipped with a roughing filter (RF) as a pretreatment unit using system dynamic (SD) modelling. STELLA was used to model the system and predict the behavior pattern, as well as the system's performance in removing turbidity, total suspended solids (TSS), BOD, COD, and phosphate. SD modelling consisted of system identification, system model framework, model structure building, system modelling, verification, and validation. Two sub-models were obtained from the main model, consisting of the RF and SSF sub-models. Results showed that dissolved oxygen (DO) and the growth rate of microorganisms played significant roles in the parameter removal. Predicted result by SD modelling showed a good fit with actual run, suggesting that factors applied in the model building were adequate to exhibit the actual system. RF removed 80.5 %–85 % of turbidity and 70.63 %–85 % of TSS, while SSF removed 48.50 %–82.43 % of turbidity, 0.92 %–46.15 % of TSS, 1.65 %–65.45 % of BOD, 22.69 %–65.22 % of COD, and 7.96 %–27.11 % of phosphate. Effluent after SSF was still having BOD and COD concentrations exceeding the governmental standard, in which increasing DO inlet and microorganism growth rate were simulated afterward. The scenarios used showed a positive impact on the removal of BOD and COD, resulting in an average concentration lower than the permissible limit (5 mg/L and 50 mg/L, respectively).

A general effectiveness-NTU modeling framework for membrane dehumidification systems

Selective membrane dehumidification is a promising technology for energy efficient air dehumidification in buildings and has been ranked as a top alternative technology by the Department of Energy. One critical area that needs to be addressed is system modeling that incorporates device mass transfer characteristics and sizing. This work develops, validates, and applies a simple effectiveness-number of transfer units (ε-NTU) modeling framework for dehumidification technologies that can be used to predict performance and determine required membrane area for a wide range of technologies, including energy recovery ventilators, vacuum membrane dehumidification, and desiccant dehumidifiers. Although the ε-NTU method is a well-established framework for sizing and modeling heat exchangers, past attempts to create εL-NTUL(meaning ε-NTU for latent loads) models for dehumidification (referred to as latent cooling): (1) don’t agree on the definition of NTUL, (2) are not fully analogous to the heat transfer definition of NTU, (3) cannot be easily applied across all technologies, (4) use humidity ratio, not vapor pressure, as the driving force, and (5) are often complex or lack a fundamental basis. In this work, we develop an effectiveness-NTU model for membrane humidity exchangers that is based on vapor pressure differences, employs a novel definition of the latent number of transfer units for dehumidification applications (NTUL) and is fully analogous to the heat transfer effectiveness-NTU models employed for sensible heat exchangers. The resulting εL-NTUL relationships are validated against experimental data with a maximum error of 1.2%. The overall methodology is applied in case studies for vacuum membrane dehumidification system sizing showing that the ratio of required membrane area per building floor area ranges between 0.1 and 3.5 depending on the building type and operating conditions. Finally, the framework is further generalized for applicability to any gas separation application.

An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems

Suitable reduced order models (ROMs) are computationally efficient tools in characterizing key dynamical and statistical features of nature. In this paper, a systematic multiscale stochastic ROM framework is developed for complex systems with strong chaotic or turbulent behavior. The new ROMs fundamentally differ from the traditional Galerkin ROM (G-ROM) or those deterministic closure ROMs that aim to minimize path-wise errors and apply mainly to laminar systems. Here, the new ROM focuses on recovering the large-scale dynamics to the maximum extent. At the same time, it also exploits cheap but effective conditionally linear functions as the closure terms to capture the statistical features of the medium-scale variables and their feedback to the large scales. The new ROM mimics the model structure of many complex systems, allowing it to be more physically explainable. In addition, physics constraints of energy-conserving nonlinearities are incorporated into the new ROM. One unique feature of the resulting ROM is that it facilitates an efficient and accurate scheme for nonlinear data assimilation, the solution of which is provided by closed analytic formulae that do not require ensemble methods. Such an analytical solvable data assimilation solution significantly accelerates computational efficiency. It allows the new ROM to avoid many potential numerical and sampling issues in recovering the unobserved states from partial observations. The closure term calibration is efficient and systematic via explicit mathematical formulae. The new ROM framework is applied to complex nonlinear systems in which the intrinsic turbulent behavior is either triggered by external random forcing or deterministic nonlinearity. The out-of-sample numerical results show that the new ROM significantly outperforms the G-ROM in both scenarios regarding reproducing the dynamical and statistical features and recovering unobserved states via the efficient data assimilation scheme.

Understanding the Capability of Future Direct-imaging Observations to Quantify Atmospheric Chemical Effects of Stellar Proton Events

Models developed for Earth are often applied in exoplanet contexts. Validation in extraterrestrial settings can provide an important test of model realism and increase our confidence in model predictions. NASA’s upcoming space-based IROUV telescope will provide unprecedented opportunities to perform such tests. Here, we use the Planetary Spectrum Generator to simulate IROUV reflected-light spectroscopic observations of flare-driven photochemical changes produced by the Whole Atmosphere Community Climate Model, part of the Community Earth System Model framework. We find that NO2 is the most observable gas to target, and integrating the signal for two days following the flare and comparing to a baseline of preflare data would achieve the highest signal-to-noise ratio. The NO2 response is much larger for K-star tidally locked planets than G-star rapidly rotating planets and does not depend strongly on O2 level. The NO2 response should be observable for planets within 3–4 pc independent of the phase angle since the amount of reflected light is larger at smaller phases, but the NO2 concentration is low near the substellar point. This work outlines a methodology for validating and ground-truthing atmospheric chemistry models developed for Earth that could be useful for the numerical exploration of exoplanets.

Effects of vertical mixing on the Lake Michigan food web: an application of a linked end-to-end earth system model framework

AbstractPhysical processes may affect ecosystem structure and function through the accumulation, transport, and dispersal of organic and inorganic materials, nutrients, and organisms; they structure physical habitat and can influence predator–prey interactions and trophic production. In the Laurentian Great Lakes, horizontal currents generally dominate, but little is known about the effects of vertical mixing on lake food webs. We developed a linked earth system model and used it to explore how vertical mixing affects the productivity of Lake Michigan (LM), the world’s fifth-largest lake, whose food web and fisheries have been adversely affected by invasive Dreissena mussels. We hypothesized that higher vertical mixing would result in higher food web biomass by making phosphorus more available to the lower food web, and that filtration by invasive mussels would counter the effects of mixing and decrease food web biomass. Using linked climate, hydrodynamics, and ecosystem models, we projected the response of LM’s food web to scenarios of different levels of vertical mixing, with and without invasive mussels. Biomass of most functional food web groups increased with increases in vertical mixing, with the greatest increases in phytoplankton and zooplankton. Increased biomass was due to the replenishment of nutrients into the euphotic zone, which enhanced growth and biomass of lower trophic levels through bottom-up effects. However, filtration by invasive mussels reduced the positive effects of mixing for most species. Future applications of the linked earth system framework will explore the effects of climate warming and nutrient reduction on fisheries production to inform fisheries managers.