System Design Optimization Research Articles

Integrated design and operational optimization of energy systems in dairies

Around 70% of worldwide industrial energy use is today based on fossil energy. Electrification of low temperature heat in this sector is pointed out as a key measure to reduce emissions. A large amount of the low temperature heat demand can be served by mature technologies, providing a possible fast way to decarbonize parts of the industry sector. Still, to reduce costs and accelerate broad, sector-wide implementation, integrating thermal and electrical energy systems will be important. Here a model is presented to analyse cost-optimal industrial energy system investments, applied to a dairy. The model uses heating, cooling and electric demands at an hourly resolution, including hourly power prices and yearly increases in energy and emission costs. The model minimizes investment, grid, energy and emission costs over a given planning period. Real data from a Norwegian dairy is used to investigate the effect on an industrial energy system subject to different future cost scenarios. The results show that an energy integrated dairy can reduce costs by 24% and emissions by 96% compared with a traditional dairy, and is cheaper to decarbonize. It is also shown that thermal energy storages provide flexibility at a low cost, eliminating the need for batteries.

A Multi-Scale method for combined design and dispatch optimization of nuclear hybrid energy systems including storage

Reliable, carbon-free power generation is increasing in importance. Nuclear-renewable hybrid energy systems (NHES) are a potential solution for current generation challenges, but design and dispatch optimization for these systems remains challenging particularly when stochastic effects, long time horizons and nonlinear modeling are needed. This work presents a multi-scale method for combining the design and dispatch optimization problems for nonlinear NHES over long time horizons. Rather than treating the entire horizon as a single optimization problem, a large number of combined optimization problems are solved for shorter samples of the horizon resulting in distributions of optimal capacities for each of the capacities being optimized. The distributions are then aggregated, and the techno-economics of each aggregate is analyzed. The result is a contextual knowledge about the techno-economic tradeoffs for the system rather than a single set of optimal capacities. This method is applied to two NHES of varying complexity. Results indicate that unit capacities for a grid-connected district energy system can be reduced by at least 18.9% by allowing for 3 periods of power import over the course of a 2000 day dispatch. The algorithm is used to solve combined optimization problems with dispatch horizons 112.5 times longer than are solvable directly.

Design, multi-aspect analyses, and multi-objective optimization of a novel trigeneration system based on geothermal and municipal solid waste energies

The study of multi-source energy systems, due to higher performance and producing various products, is an interesting subject. The current study designs a multi-source system including a gas turbine cycle with CH4 and municipal solid waste digester, a geothermal system with an organic Rankine, organic flash, and a double-effect absorption refrigeration subsystem to produce power, cooling, and heating loads simultaneously. The thermodynamic and economic analyses are utilized to estimate the system’s performance and multi-objective optimization has been applied to define the optimum state of the system concerning two scenarios. The results indicate that the system provides 423.4 kW net power, 384.7 kW heating load, and 106.3 kW cooling load with 39.28% exergetic efficiency and 6.67 years payback period. These values are improved to 664.9 kW net power, 446.5 heating load, 42.01% exergetic efficiency, and 6.05 years payback period at the best optimum scenario. The sensitivity analysis reveals that the air compressor’s pressure ratio mainly impacts the net power, heating load production, and net present value. The cooling load production and exergetic efficiency are mainly affected by the air preheater effectiveness variation.

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer.

When developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties. The aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer. The generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs. To advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast. The proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve performance. We allow for the creation of temporary artificial network partitions during which agents synchronize only locally within their group in a way that does not compromise the correctness of a simulation. We have built a distributed engine, CloudCity, which uses this idea to improve the locality of computation, communication, and synchronization in such simulations. We experimentally evaluate the performance of our system on a benchmark of simulation workloads and compare it against other popular BSP-like systems, obtaining insights into the impact of various system design choices and optimization on simulation engine performance.

Experimental study on propulsion performance of unmanned sailboat with arc sail

In recent years, the unmanned sailboats, which constantly obtain energy from the environment for propulsion, have attracted great attention from global researchers. The existing studies on unmanned sailboats mainly concentrate on navigation path planning, control system design, speed prediction and attitude optimization design, but little emphasis is put on the sail that provides the core propulsion. This study carries out orthogonal analysis and CFD simulations to analyze the influence of coupling parameters on the aerodynamic performance of arc sail, and determines an arc sail suitable for the existing Petrel unmanned sailboats. The structure and turning logic of the selected arc sail is elaborated in detail. The pool experiment with adjustable initial apparent wind angle of the sailboat is designed to verify the turning logic of the arc sail and the performance of the sailboat. Finally, an outdoor experiment is performed to validate the navigation performance of the Petrel unmanned sailboat equipped with the selected arc sail in actual sea conditions.

Nonlinear excitation and mesh characteristics model for spiral bevel gears

Accurate and rapid calculation of nonlinear excitation (NE) and loaded mesh characteristics (LMC) of gear pairs is a key to achieving rapid iterative optimization of gear system designs. Therefore, an analytical model (AM) was proposed for the accurate and numerically efficient calculation of NE and LMC for application to spiral bevel gears (SBG). First, a tooth contact analysis of an SBG was completed using Coons surface technology and mesh theory to determine the contact path and unloaded transmission error (UTE). Thus, an AM for the principal and relative normal curvatures of the SBG was derived using the Euler's formula. Based on the traditional calculation method for a Hertz contact ellipse, the relation between the relative curvatures was introduced to derive the formulas for the major and minor axes using the Muller's method, which could improve the programmability and avoid numerical instability of the program. Then, analytical models for the contact ellipse and pressure distribution on the contact tooth surfaces were derived using the Hertz model and geometric theory. Subsequently, based on a single tooth LMC, infinitesimal method, elasticity, and series stiffness model, a single mesh stiffness (SMS) model for SBGs was developed, which incorporates transverse tooth stiffness, transverse gear foundation stiffness, axial tooth stiffness, axial gear foundation stiffness, and Hertz nonlinear contact stiffness. Based on the SMS and UTE, compliance and load matrices were established, and the time-varying mesh stiffness, loaded transmission error, mesh force, and multi-tooth LMC models of the SBG were determined. Finally, the proposed method was validated against nonlinear FE simulations using examples. Compared to FE simulations, the proposed method requires significantly less computational effort and can be further extended to optimization or system analysis problems.

Simultaneous and Global Optimizations of LNG-Fueled Hybrid Electric Ship for Substantial Fuel Cost, CO2, and Methane Emission Reduction

Natural gas (NG) is a promising low-carbon fuel to replace diesel for heavy-duty marine propulsion with prominent fuel cost and carbon dioxide equivalent (CDE) emissions reduction potential. However, using NG in compression ignition (CI) engines presents several inherent drawbacks. This research addresses the central issue of an NG-diesel CI engine with increased methane emissions, resulting in minor CDE emission reduction. The hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) emissions are minimized in addition to fuel consumption and carbon dioxide emission reduction as in the current practice. The simultaneous optimizations of powertrain component sizes and the NG-engine hybrid electric propulsion control are introduced to minimize NG fuel consumption and CDE emissions globally. The top-level powertrain component sizing and bottom-level optimal power control and energy management are conducted simultaneously to achieve global optimization with balanced fuel cost and CDE emissions reductions. The new method reduced the fuel cost by about 75 percent and well-to-wake CDE emissions by about 40 percent over the traditional diesel-mechanical propulsion for the test ferry running under an actual operation profile. The research opened a new path to the global design and control optimization of the NG-diesel CI engine-powered hybrid electric marine propulsion system.

Design and Multiobjective Optimization of Dual-Circulation Cooling System Considering Electro-Thermal Coupling for IPMSM

Benefiting from high power density, high reliability, and wide speed range, interior permanent magnet synchronous motors (IPMSM) have received unprecedented attention. However, due to torque ripple and heat dissipation difficulties caused by the compact structure, the development of IPMSM is not smooth in high-end applications such as electric vehicles. In this article, a dual-circulation cooling system consisting of an external housing water-cooling cycle and an internal rotor-stator air-cooling cycle based on improved magnetic circuit design is innovatively proposed. While the electromagnetic characteristics are substantially improved, the temperature rise problem is also significantly suppressed and weakened. Furthermore, to further optimize multiphysics performance for the IPMSM equipped with this system, an improved multiobjective optimization model considering electro-thermal coupling is studied. Optimization effect is greatly improved by the flexible application of segmented orthogonal experiments, correlation/interaction analysis, and intelligent surrogate model/advanced algorithm. Computational fluid dynamics (CFD) and finite element analysis (FEA) highlight the advantages of proposed dual-circulation cooling system and improved optimization model. Their design rationality, effectiveness, and engineering practicability have been well verified through a series of comparative analyses.

MRAM-Based Cache System Design and Policy Optimization for RISC-V Multi-Core CPUs

On-chip cache, being the crucial constituent of central processing unit (CPU), accounts for a pivotal portion of the system’s overall power consumption. The typical SRAM-based cache is vulnerable to high static leakage power consumption, while multiple challenges encountered in scaling up its capacity. In the paper, magnetic random-access-memory (MRAM) cache is considered as a promising candidate in RISC-V CPU systems. After analyzing the performances of single-core, dual-core, quad-core, and octa-core systems, a quad-core RISC-V based CPU system with a two- level hierarchical cache structure is designed, in which Spin-Orbit Transfer (SOT)-MRAM is adopted as the L1 private cache and Spin-Transfer Torque (STT)-MRAM as the L2 shared cache. Details on the microarchitecture of the multi-core CPU design are provided. Energy evaluation shows that this quad-core cache system can achieve 66.4% leakage power saving and 34.5% total power saving compared to SRAM. For high efficiency of data-exchange in the novel MRAM CPU systems, three types of characteristics optimizing methods are proposed. An optimized cache-coherency protocol incorporating non-inclusive policy is proposed. The quad-core system is updated by adding the coherence control module and a directory in the design scheme. The simulation results on multiple dimensions show that the policy of the hybrid MRAM memory is a promising candidate for multi-core RISC-V system, with the benefits of low-power consumption and high hit rate. Finally, the peripheral circuitry and manufacturing cost for the implementation of the MRAM cache is discussed in the paper.

Key technology and application of gun agile optimization design based on dynamic simulation

The new generation of artillery equipment is showing the development trend of lightweight, long-range, intelligent et al. However, the traditional development mode based on physical prototype test cannot meet these needs of equipment development. In the development, there are some problems, such as lack of systematic guidance in the overall design scheme, low degree of digitalization and parameterization of the model, and long period of serial iterative development, which need to be solved by optimization design. Parametric modeling technology, hard point driven template model technology and structural optimization design technology are briefly introduced, which are the foundation of equipment optimization design. These key technologies, combined with the overall scheme design, chassis system design and ammunition loading timing optimization design of a large-caliber tracked self-propelled gun, showing the basic development route of artillery performance improvement. The optimization design of artillery type equipment are summarized in recent years, which provide reference for other similar work.

Integrated optimization in operations control and systems design for carbon emission reduction in building electrification with distributed energy resources

Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m2/year.

Optimized design and integration of energy storage in Solar-Assisted Ground-Source Heat Pump systems

The integrated use of multiple renewable energy sources to increase the efficiency of heat pump systems, such as in Solar Assisted Geothermal Heat Pumps (SAGHP), may lead to significant benefits in terms of increased efficiency and overall system performance especially in extreme climate contexts, but requires careful integrated optimization of the different system components. In particular, thermal storages take a fundamental role in optimizing the integration of renewable energy sources and the system operation. This work investigates the potential design optimization of a SAGHP system in a mountain site by exploring many different alternatives to optimize the mutual relationship between the solar field, the geothermal field and the water thermal storages. This is done through an original simulation-based multi-objective optimization framework considering energy efficiency and economic feasibility, which allows appraising the impact of the different design alternatives on the overall system performance and on the dynamics of the different system components. Results identify a set of optimized system configurations that optimize the integrated exploitation of the different thermal sources showing a potential increase of the overall system performance leading to 34% lower global cost compared to the initial design. High robustness of the optimal design solutions is reported with respect to the current context of high economic uncertainty.

Design and multi-objective optimization of a multi-generation system based on PEM electrolyzer, RO unit, absorption cooling system, and ORC utilizing machine learning approaches; a case study of Australia

The geothermal systems' capability to produce various products persuades the design of a novel hybrid system, including power, cooling, freshwater, and hydrogen. Australia, with numerous geothermal sources and the tendency of hydrogen utilization as fuel, is considered a case study. The designed system is analyzed from energy, exergy, and economic viewpoints. Also, the artificial neural network is implemented to optimize the system's operation. Hence, the accuracy of four artificial neural networks in optimizing and predicting systems' performance is compared. Furthermore, four double-objective and four triple-objective optimization scenarios are considered to achieve the best optimum state from different viewpoints. The mean absolute Error value of 2.28×10−14 to predict the exergetic efficiency in the testing procedure is the best algorithm. The system provides 1263 kW net power with 39.89% exergy efficiency and 2.13 years of payback period at the base condition. The exergy efficiency-net present value scenario represents the best performance in which the net power, hydrogen, and freshwater production rates reach 3946 kW, 8.73 kg/h, and 152 kg/h, respectively. Subsequently, the exergy efficiency, payback period, and net present value are estimated at 46.27%, 1.84 years, and 19.52 M$.

A Model-Assisted Probability of Detection Framework for Optical Fiber Sensors

Optical fiber sensors (OFSs) represent an efficient sensing solution in various structural health monitoring (SHM) applications. However, a well-defined methodology is still missing to quantify their damage detection performance, preventing their certification and full deployment in SHM. In a recent study, the authors proposed an experimental methodology to qualify distributed OFSs using the concept of probability of detection (POD). Nevertheless, POD curves require considerable testing, which is often not feasible. This study takes a step forward, presenting a model-assisted POD (MAPOD) approach for the first time applied to distributed OFSs (DOFSs). The new MAPOD framework applied to DOFSs is validated through previous experimental results, considering the mode I delamination monitoring of a double-cantilever beam (DCB) specimen under quasi-static loading conditions. The results show how strain transfer, loading conditions, human factors, interrogator resolution, and noise can alter the damage detection capabilities of DOFSs. This MAPOD approach represents a tool to study the effects of varying environmental and operational conditions on SHM systems based on DOFSs and for the design optimization of the monitoring system.

Thermal hydraulic design and mass optimization of a 100-kWe S-CO2 cooled Mars-surface fission reactor system

Fission reactor with electrical power of 100 kW to megawatt is a promising power option for upcoming deep space exploration. To reduce the number of space launches and the costs, it is necessary to design an energy supply system coupling fission reactor with energy conversion system of high efficiency and compactness. The combination of gas-cooled fast reactor and supercritical carbon dioxide (S-CO2) Brayton cycle has the advantages of simple system layout, compact structure and high thermal efficiency.This paper aims to carry out researches on the thermal hydraulic design and mass optimization of 100kWe reactor system for Mars-surface application.An analysis code which includes thermodynamic models for different components in the system, as well as mass evaluation models for radiator, recuperator and reactor, is developed and validated to provide an analysis tool. An S-CO2 Brayton cycle directly cooled reactor system is proposed, which applies Tube-In-Duct fuel assembly to make up the core and heat pipe radiator as heat rejection system. The thermal hydraulic evaluation of the reactor core, thermodynamic performance evaluation as well as overall mass evaluation of the whole system are carried out to achieve an optimized 100kWe fission reactor system for Mars surface with minimum mass. The optimal design has an operating pressure of 16 MPa, core outlet temperature of 600 °C, radiator panel area of 120 m2 and an overall mass of 1862.98 kg considering the reactor and key components of Brayton cycle. S-CO2 Brayton cycle directly cooled reactor system has a comparable specific mass as He-Xe cooled reactor with lower operating temperature, which is beneficial for material selection and provides another option for space reactor development.

Holistic fleet optimization incorporating system design considerations

AbstractThe methodology described in this article enables a type of holistic fleet optimization that simultaneously considers the composition and activity of a fleet through time as well as the design of individual systems within the fleet. Often, real‐world system design optimization and fleet‐level acquisition optimization are treated separately due to the prohibitive scale and complexity of each problem. This means that fleet‐level schedules are typically limited to the inclusion of predefined system configurations and are blind to a rich spectrum of system design alternatives. Similarly, system design optimization often considers a system in isolation from the fleet and is blind to numerous, complex portfolio‐level considerations. In reality, these two problems are highly interconnected. To properly address this system‐fleet design interdependence, we present a general method for efficiently incorporating multi‐objective system design trade‐off information into a mixed‐integer linear programming (MILP) fleet‐level optimization. This work is motivated by the authors' experience with large‐scale DOD acquisition portfolios. However, the methodology is general to any application where the fleet‐level problem is a MILP and there exists at least one system having a design trade space in which two or more design objectives are parameters in the fleet‐level MILP.

Platform development and gliding optimization of a robotic flying fish with morphing pectoral fins

The aquatic-aerial robot with the free interface crossing can enhance adaptability in complex aquatic environments. However, its design is extremely challenging for the striking discrepancies in propulsion principles. The flying fish in nature exhibits remarkable multi-modal cross-domain locomotion capability, such as high-maneuvers swimming, agile water-air crossing, and long-distance gliding, providing extensive inspiration. In this paper, we present a unique aquatic-aerial robotic flying fish with powerful propulsion and a pair of morphing wing-like pectoral fins to realize cross-domain motion. Furthermore, to explore the gliding mechanism of flying fish, a dynamic model with a morphing structure of pectoral fins is established, and a double deep Q-network-based control strategy is proposed to optimize the gliding distance. Finally, experiments were conducted to analyze the locomotion of the robotic flying fish. The results suggest that the robotic flying fish can successfully perform the ‘fish leaping and wing spreading’ cross-domain locomotion with an exiting speed of 1.55 m s−1 (5.9 body lengths per second, BL/s) and a crossing time of 0.233 s indicating its great potential in cross-domain. Simulation results have validated the effectiveness of the proposed control strategy and indicated that the dynamical adjustment of morphing pectoral fins contributes to improving the gliding distance. The maximum gliding distance has increased by 7.2%. This study will offer some significant insights into the system design and performance optimization of aquatic-aerial robots.

Design and operational optimization of a methanol-integrated wind-solar power generation system

Wind and solar energy are rapidly being merged into electricity grids in China. High penetration of variable renewable electricity drives the development of energy storage with low cost, high flexibility and utility-scale. To this end, a methanol-based energy storage system is proposed to meet regional power demand by combining a hybrid wind-solar source. This work studies capacity configuration and logistics scheduling at the hourly level with the minimum power generation cost. The round-trip efficiency reaches 41.5%, and the levelized cost of electricity is 0.148 $/kWh. The wind-solar hybrid system improves the system efficiency and economy compared with separated wind or solar systems. Taking methanol as the hydrogen carrier significantly reduces the storage scale of hydrogen. In terms of regions with larger fluctuation of renewable energy, methanol-based energy storage is more cost-effective than hydrogen energy storage.

Multi-fidelity modeling and analysis of a pressurized vessel-pipe-safety valve system based on MOC and surrogate modeling methods

A pressurized vessel-pipe-safety valve (PVPSV) combination is a commonly used configuration in nuclear power plants, and a good numerical model is essential for the system design, sizing and performance optimization. However, owing to the large-scale and cross-scale features, it is still a challenge to build a system level numerical model with both high accuracy and efficiency. To overcome this, a novel system level modeling method which can synthesize the advantages of various models is proposed in this paper. For system modeling, the analytical approach, the method of characteristics (MOC) and the surrogate model approach are respectively adopted to predict the dynamics of the pressure vessel, the connecting pipe and the safety valve, and different models are connected through data interfaces. With this system model, dynamic simulations were carried out and both the stable and the unstable system responses were obtained. For the model verification purpose, the simulation results were compared with those obtained from experiments and full CFD simulations. A good agreement and a better efficiency were obtained, verifying the ability of the model and the feasibility of the modeling method proposed in this paper.