Optimized genome-wide CRISPR screening enables rapid engineering of growth-based phenotypes in Yarrowia lipolytica
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Robertson, Nicholas R.↗
SEARCH · Engineering Papers
Results for “engine optimization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Optimal performance of a three-stroke heat engine in the microscopic regime
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Biswas, Tanmoy (ORCID:000000017232976X)↗
Optimized piston temperature control in gaseous fuel hydrogen engine system
Operating a gaseous fuel engine system includes combusting a mixture containing a gaseous hydrogen fuel and air in a cylinder of an engine, varying an operating parameter of the engine to which a crown surface temperature of a piston within the cylinder is responsive, and populating a temperature model based on a value of the varied operating parameter. Operating a gaseous fuel engine system further includes operating an oil spray apparatus to spray oil onto the piston based on the populated temperature model, and maintaining the crown surface temperature of the piston between a high temperature limit and a pre-ignition mitigation temperature limit based on the operating of the oil spray apparatus. Related apparatus and control logic is also disclosed.
Bochart, Michael R.↗
Machine learning models for PDE constrained optimization
Partial differential equation (PDE)-constrained optimization problems arise in a variety of scientific and engineering applications, such as topology optimization, electrodynamics, fluid dynamics, and structural dynamics. However, these problems are often challenging and computationally expensive to solve, due to the need to solve the PDEs within the optimization loop. One approach to reducing the computational cost of these methods while providing convergence guarantees is through inexact trust region methods; this method uses lower fidelity solutions of the PDE at early stages of the optimization and adjusts the required accuracy of inexact PDE solvers as the optimization progresses. In this work, we explore the use of machine learning based surrogate models with these inexact trust region methods. We first demonstrate the potential of this approach by using Gaussian processes as the surrogate model and test this on a simple PDE-constrained optimization problem. We then document explorations into improving the computational costs of evolutional deep neural network / neural Galerkin methods, with the eventual goal of using these methods with the inexact trust region algorithms. We are able to speed up these approaches, albeit at the cost of lower accuracy.
97 MATHEMATICS AND COMPUTING↗
Level set topology optimization in Firedrake
Lestofire is an academic software for the automated design of engineering components. It finds the optimal geometry of a system to minimize a given cost function while constrained by a given set of physics equations. Using a level set function to describe the geometry, lestofire uses shapes derivative to evolve the system until its optimal shape. It leverages the compilation of variational forms in Firedrake (https://www.firedrakeproject.org/) and the automated calculation of shape derivatives via pyadjoint (http://www.dolfin-adjoint.or/en/latest/) to allow fast prototyping of different physics, cost functions and design parametrizations.
Salazar De Troya, Miguel↗
Multiphysics simulations of Self-Regulating performance of an optimized molten metal fuel microreactor design
Advanced microreactors are expected to play an indispensable role in reliable energy solutions for civilian/ military applications in off-grid regions and space applications such as power supplies for Lunar and Mars bases. An innovative fast neutron spectrum heat pipe microreactor (HP-MR) concept was recently proposed with two unique features: molten U-Mn fuel and heat pipes as the heat removal mechanism. The microreactor was designed to be self-regulated, solely relying on reactivity feedbacks from fuel temperature. The reactor design has been further optimized to provide sufficient engineering safety margin and effective heat removal. In this study, the self-regulating performance of the optimized molten metal fuel microreactor was investigated by multi-physics simulations (neutronics, heat-transfer and heat pipe modeling) based on the MOOSE MultiApps system. In conclusion, the responses of the reactor to a series of transient scenarios were simulated to demonstrate the exceptional and inherent safety features of the novel microreactor concept.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Optimal experimental design: Formulations and computations
Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.
97 MATHEMATICS AND COMPUTING↗
Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation
Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.
15 GEOTHERMAL ENERGY↗
Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles
This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing. This work is experimentally experimentally validated via a sequence of EIL studies intended for evaluating the computational costs and fuel savings associated with these algorithms. These EIL studies involve the closed-loop validation of the proposed control strategies, both individually and combined. These studies show that this hierarchy of algorithms is capable of running online, with the round-trip communication delay inherent in EIL simulation being one of the key factors affecting the EIL results. Moreover, the EIL studies are encouraging, both in terms of the successful hierarchical integration of the underlying algorithms and also in the resulting fuel savings seen in the EIL tests. In particular, the EIL results suggest that an aggressive overall goal of reducing vehicle fuel consumption by 15-20% or more is potentially achievable, especially in urban/suburban scenarios.
33 ADVANCED PROPULSION SYSTEMS↗
Development of prechamber enabled mixing-controlled combustion strategy for ultra-low methane emissions from lean burn natural gas engines
This numerical study explores the optimization of Prechamber Enabled Mixing-Controlled Combustion (PC-MCC) using natural gas in heavy-duty engines, aiming to enhance combustion efficiency to minimize methane slip and NOx emissions. The approach involves a prechamber ignition system, distinct from conventional spark ignition (SI) systems, to initiate combustion of direct injected natural gas. By leveraging the robust ignition characteristics of the prechamber, the PC-MCC method demonstrates significant potential in achieving efficient combustion akin to diesel engines but with lower greenhouse gas emissions. The research evaluates the effects of various geometric and operational parameters on the combustion process and emissions, including prechamber volume, nozzle diameter, direct injector (DI) geometry, and engine operating strategies. Computational Fluid Dynamics (CFD) simulations are utilized, focusing on a heavy-duty, single-cylinder engine modeled after the Caterpillar C9.3B engine. Key findings indicate that a prechamber volume of 3 cc, coupled with a nozzle diameter of 2.75 mm for two prechamber holes, strikes an optimal balance between combustion efficiency and emissions reduction. This configuration ensures robust combustion across a range of operating conditions while maintaining methane slip within targeted limits. Further investigation into DI geometry shows the significance of the injector umbrella angle and nozzle diameter in shaping the fuel-air mixing and combustion dynamics. An umbrella angle of 130° and a nozzle diameter of 300 microns are identified as optimal, promoting rapid and efficient combustion with minimized methane and NOx emissions. The study also investigates the impact of injection timing and pressure, highlighting their roles in controlling combustion timing and influencing emissions levels. Advanced injection timing is found to be crucial in achieving the desired low methane slip, whereas retarded injection timing assists to reduce NOx emissions while having a slight increase in methane emissions. Operating strategies incorporating various levels of Exhaust Gas Recirculation (EGR) are assessed for their effectiveness in further reducing emissions. The research demonstrates that a judicious combination of internal hot EGR and careful calibration of DI pressure and SOI timing can achieve significant reductions in NOx emissions while keeping methane slip under control. Specifically, an internal EGR level of 15%–25%, combined with DI pressures of 200–300 bar and injection timings at or after top dead center, is recommended. These findings contribute valuable insights into the development of advanced combustion techniques for natural gas engines, offering a viable pathway to reduce methane slip without compromising engine efficiency or performance. The PC-MCC system presents a promising solution for the future of heavy-duty natural gas engine technology to reduce methane emissions.
Nsaif, Osama↗
A numerical study on the sooting tendencies of bio-derived fuels for spark-ignition engines
While more efficient vehicles have been developed over the decades, their performance is limited by the properties of existing fuels. In response, the Co-Optimization of Fuels and Engines Initiative (Co-Optima) under the U.S. Department of Energy (DOE) has developed a rigorous screening approach to identify the most promising biomass-derived blendstocks that are suitable for advanced spark-ignition (SI) engines. A detailed kinetic model has also been developed to predict the combustion properties of the selected blendstocks. This kinetic model has been designed mainly targeting ignition and flame propagation properties, and it has not previously been validated for soot formation. In this work, we numerically predicted the sooting tendency of 20 Co-Optima SI blendstocks using the Co-Optima kinetic model. The sooting tendencies are determined quantitatively using the Yield Sooting Index (YSI) methodology. As shown in Fig. 1, the predicted YSIs show good agreement with measurements, except for 2,5-dimethylfuran. We also quantify the sensitivity of the YSI predictions to aromatic growth reactions and fuel decomposition reactions of the test fuels. It is found that PAH growth reactions have little impact on YSIs for all 20 fuels under investigation. On the other hand, fuel decomposition reactions have a significant influence on the YSI of 2,5-dimethylfuran. Perturbing their reaction rates by a factor of two is shown to lead to 11 % YSI prediction uncertainty.
09 BIOMASS FUELS↗
Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach
Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.
36 MATERIALS SCIENCE↗
Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods
Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.
MATHEMATICS AND COMPUTING↗
DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering
With advances in machine learning (ML)-assisted protein engineering, models based on data, biophysics, and natural evolution are being used to propose informed libraries of protein variants to explore. Synthesizing these libraries for experimental screens is a major bottleneck, as the cost of obtaining large numbers of exact gene sequences is often prohibitive. Degenerate codon (DC) libraries are a cost-effective alternative for generating combinatorial mutagenesis libraries where mutations are targeted to a handful of amino acid sites. However, existing computational methods to optimize DC libraries to include desired protein variants are not well suited to design libraries for ML-assisted protein engineering. To address these drawbacks, we present DEgenerate Codon Optimization for Informed Libraries (DeCOIL), a generalized method which directly optimizes DC libraries to be useful for protein engineering: to sample protein variants that are likely to have both high fitness and high diversity in the sequence search space. Using computational simulations and wet-lab experiments, we demonstrate that DeCOIL is effective across two specific case studies, with potential to be applied to many other use cases. DeCOIL offers several advantages over existing methods, as it is direct, easy-to-use, generalizable, and scalable. With accompanying software, DeCOIL can be readily implemented to generate desired informed libraries.
59 BASIC BIOLOGICAL SCIENCES↗
Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows
Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.
42 ENGINEERING↗
Deep reinforcement learning to discover multi-fuel injection strategies for compression ignition engines
Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.
33 ADVANCED PROPULSION SYSTEMS↗
Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning
Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. Here, we address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.
97 MATHEMATICS AND COMPUTING↗