High Energy Cost Research Articles

Effect of thermal gas quenching on NOx production by atmospheric pressure rotating arc Plasma: A pathway towards Eco-Friendly fertilizer

Plasma nitrogen fixation allows an alternative ammonia production path compared to the classical industrial Haber-Bosch process, due to the emergence of low-cost and environment friendly renewable energy. However, the high energy cost due to high-temperature reaction condition and reverse reactions in typical “warm” plasmas (gliding arc and rotating gliding arc) lies as a bottleneck of its industrial application. As a possible solution to enhance the efficiency of the process, the effect of thermal gas quenching on nitrogen fixation by rotating arc plasma are investigated. The NOx (i.e. NO, NO2) yield, energy cost, and NOx selectivity are obtained under variable operating conditions (i.e. oxygen fraction in the feed gas, arc length, specific energy input, varying quench gas and quenching ratio). With the help of quenching, the maximum achievable NOx concentration was enhanced from 5.16 to 5.8%. The enhancement is achieved by mitigating loss reactions through quenching and it is supported by the integration of experimental findings, plasma diagnostics using optical emission spectroscopy, and the application of a zero-dimensional chemical kinetic model.

Novel adaptive quantization methodology for 8-bit floating-point DNN training

There is a high energy cost associated with training Deep Neural Networks (DNNs). Off-chip memory access contributes a major portion to the overall energy consumption. Reduction in the number of off-chip memory transactions can be achieved by quantizing the data words to low data bit-width (E.g., 8-bit). However, low-bit-width data formats suffer from a limited dynamic range, resulting in reduced accuracy. In this paper, a novel 8-bit Floating Point (FP8) data format quantized DNN training methodology is presented, which adapts to the required dynamic range on-the-fly. Our methodology relies on varying the bias values of FP8 format to fit the dynamic range to the required range of DNN parameters and input feature maps. The range fitting during the training is adaptively performed by an online statistical analysis hardware unit without stalling the computation units or its data accesses. Our approach is compatible with any DNN compute cores without any major modifications to the architecture. We propose to integrate the new FP8 quantization unit in the memory controller. The FP32 data from the compute core are converted to FP8 in the memory controller before writing to the DRAM and converted back after reading the data from DRAM. Our results show that the DRAM access energy is reduced by 3.07×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} while using an 8-bit data format instead of using 32-bit. The accuracy loss of the proposed methodology with 8-bit quantized training is ≈1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 1\\%$$\\end{document} for various networks with image and natural language processing datasets.

A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering

Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.

A review of electrochemical cells and liquid metal battery (LMB) parameter development

Liquid Metal Battery (LMB) technology is a new research area born from a different economic and political climate that has the ability to address the deficiencies of a society where electrical energy storage alternative are lacking. The United States government has begun to fund scholarly research work at its top industrial and national laboratories. This was to develop liquid metal battery cells for energy storage solutions. This research was encouraged during the Cold War battle for scientific superiority. Intensive research then drifted towards high energy rechargeable batteries, which work better for automobiles and other applications. Intensive research has been carried out on the development of electrochemical rechargeable all-liquid energy storage batteries. The recent request for green energy transfer and storage for various applications, ranging from small-scale to large-scale power storage, has increased energy storage advancements and explorations. The criteria of high energy density, low cost, and extensive energy storage provision have been met through lithium-ion batteries, sodium-ion batteries, and Liquid Metal Battery development. The objective of this research is to establish that liquid metal battery technology could provide research concepts that give projections of the probable electrode metals that could be harnessed for LMB development. Thus, at the end of this research, it was discovered that the parameter estimation of the Li//Cd-Sb combination is most viable for LMB production when compared with Li//Cd-Bi, Li-Bi, and Li-Cd constituents. This unique constituent of the LMB parameter estimation would yield a better outcome for LMB development.

The evaluation of renewable energy alternatives in Turkey using intuitionistic-fuzzy EDAS methodology

In recent years, high energy costs, increases in carbon emissions, and concerns about energy supply security have led countries to prioritize renewable energy sources in their sustainable energy policies. The selection and ranking of alternative renewable energy sources is a critical issue in establishing an effective energy policy and ensuring environmental improvement at the national and global levels. This study evaluates Turkey’s best renewable energy options using the institutional fuzzy assessment based on distance to mean solution (IF-EDAS) method and aims to find Turkey’s best renewable energy alternative. The decision model alternatively uses wind, solar, geothermal, biomass, wave, hydroelectric, and hydrogen energy options. According to the study’s empirical findings, while the best alternative renewable energy source for Turkey is solar energy, the best criterion in terms of criteria evaluation is “Technology Maturity”. The study also carried out sensitivity analysis, and the results were shared.

Critical Review of the Metallurgical Operation of the Greek Nickel Industry and Perspectives, based on the recent Industrial Experience on Smelting Reduction Process

The mining and industrial production of Greek ferronickel industry LARCO have ceased since 30.07.2022. The last LME nickel price crisis, exclusion from bank loaning, extremely high cost of electrical energy, management issues, resulted inter alia, in a prohibitive operational cost. Nevertheless, LARCO faces the challenge of restructuring and reoperation in the future, given that privatization is in progress, including the acquisition of the company’s assets to a joint venture enterprise.
 Within this framework, the current paper aims to contribute to the discussion for developing a new management strategy for the optimization of pyrometallurgical process, focusing on the critical step of smelting reduction. Based on industrial experience concerning open bath submerged arc electric furnaces, factors which critically affect the safety and cost-effectiveness of smelting reduction are detected and presented by means of case studies, being mainly classified as following: i) Optimal raw materials’ feed management, including laterite ores (domestic or not), solid fuels and electrode paste. Moreover, taking for granted the need for Ni-rich ‘foreign’ ores to increase the recovery and reduce the cost of the process, the extent of dependence on Fe-rich domestic ores is investigated, in order a safe and stable operation to be ensured. ii) Focus on preventive maintenance management. Substantial increase of the facilities’ operational index and cost saving is obtained, as proved by operational case studies. iii) Modern human resources management strategy. Their economical footprint is further discussed.

Azolla Plant Production and Their Potential Applications

Globally, the human population is growing at an alarming rate, reducing land coverage over time. In the modern world, lifestyle changes, the nature of work, and food habits increase the incidence of serious diseases in animals, and human activity influences the environment, such as the use of chemical fertilizers for agriculture, large terrestrial ecosystems all over the biosphere, such as deforestation of plants, which could not sink from atmospheric CO2, and the production of fuels for energy, which would increase the need for fossil fuels but would also deliver low energy fuels at a high energy cost. To overcome the above problems, Azolla plants perform well since they can be grown at low labor costs, on small plots of land, and for a variety of purposes including animal feed/livestock, poultry and fish production, environmental remediation, biofertilizer and biocontrol for mosquito repellents, carbon sequestration of CO2, and bioenergy effectiveness all year. As a result of their low impact on the environment and human health, Azolla plants are becoming increasingly important. The purpose of this review is to provide evidence of Azolla plant production and its potential role in various applications for a greener, more sustainable approach. This review was progressive in that it assessed and produced peer-reviewed papers related to Azolla plant production and its potential role in different applications for a sustainable greener approach. Based on the findings of reputable educational journals, articles were divided into three categories: methods used to produce the nutritional composition of Azolla, environmental factors that affect the efficiency of Azolla plants, and strain improvement of Azolla for enhanced multipurposes and techniques that are currently being used to meet Azolla plants production and its prospective applications for different sustainable greener approaches. The present findings indicate that Azolla is a rich source of protein, which has a clear benefit in offsetting a portion of the nutritional needs of animal/livestock, poultry, and fish production with what is effectively a low-cost dietary supplement, biocontrol of mosquito repellent, environmental bioremediation, biofertilizer, carbon sequester of CO2, and bioenergy for the potential need of Azolla plant applications. Azolla plants have long been recognized for their benefits in greener and more sustainable lifestyles, as well as quality enhancement and bio-based economy over traditional approaches. Because it relied on natural resources and utility green production, this review’s recovery was chosen as an appropriate and environmentally sound solution for a long and healthy lifestyle.

Global omics study of Tetraselmis chuii reveals time-related metabolic adaptations upon oxidative stress

Microalgae species encounter oxidative stress in their natural environments, prompting the development of species-specific adaptation mechanisms. Understanding these mechanisms can offer valuable insights for biotechnological applications in microalgal metabolic manipulation. In this study, we investigated the response of Tetraselmis chuii, an industrially important microalga, to H2O2-induced oxidative stress. Exposure to 0.5-mM H2O2 resulted in reduced cell viability, and higher concentrations led to a drastic decline. After 1 h of exposure to H2O2, photosynthetic capacity (Qy) was negatively impacted, and this reduction intensified after 6 h of continuous stress. Global multi-omics analysis revealed that T. chuii rapidly responded to H2O2-induced oxidative stress within the first hour, causing significant changes in both transcriptomic and metabolomic profiles. Among the cellular functions negatively affected were carbon and energy flow, with photosynthesis-related PSBQ having a 2.4-fold downregulation, pyruvate kinase decreased by 1.5-fold, and urea content reduced by threefold. Prolonged exposure to H2O2 incurred a high energy cost, leading to unsuccessful attempts to enhance carbon metabolism, as depicted, for example, by the upregulation of photosystems-related PETC and PETJ by more than twofold. These findings indicate that T. chuii quickly responds to oxidative stress, but extended exposure can have detrimental effects on its cellular functions.Key points• 0.5-mM H2O2–induced oxidative stress strongly affects T. chuii• Distinct short- and long-term adaptation mechanisms are induced• Major metabolic adaptations occur within the first hour of exposure

Research on Optimized Design of Rural Housing in Cold Regions Based on Parametrization and Machine Learning

With the rapid development of urbanization, the emergence of more self-built buildings in the countryside has brought about energy waste problems and decreased comfort. Achieving the low-carbon goal and improving the quality of the human living environment through architectural and planning means have become vital issues. In this study, from a parametric perspective, model building and performance simulation are carried out using Rhino and Grasshopper, and a multi-objective optimization method and a neural network model are used as the theoretical basis to train the prediction model after data collection and processing. The model validation of R2 = 0.988 and MSE = 0.0148 indicates that the model can accurately reflect the program’s performance. By establishing a rapid prediction model for the performance of rural residential buildings, decision-makers can perform performance predictions under various parameter combinations at the early design stage, facilitating the screening of building types with high energy consumption and costs. The method can improve the efficiency of decision-making at the early stage of design, help save decision-making costs by screening high-energy-consuming building types, improve the living conditions of residents, reduce carbon emissions, and contribute to the sustainable development of residential building renewal design in rural areas.

Flow-based Service Time optimization in software-defined networks using Deep Reinforcement Learning

A Ternary Content-Addressable Memory (TCAM) constitutes a memory variant employed within Software Defined Networking (SDN) node flow tables. These TCAMs deliver swift processing, enabling rapid parallel lookups. Nonetheless, due to their high energy consumption and cost, TCAMs have limited dimensions. This size constraint influences rule capacity, and suboptimal rule management can degrade network quality of service. Although different techniques for flow table management have been proposed during recent years, such as eviction, idle and hard timeout mechanisms, this paper proposes a Deep-Reinforcement Learning (DRL) solution, namely DRL-Idle, that is able to maximize the service time of the flows in an SDN network without considering any assumption about their status throughout time. By means of a continuous learning, DRL-Idle is able to also minimize the number of rule installations that are required to serve the target flows. Based on the idea of dynamically modifying the idle timeout value of the flows according to their needs, DRL-Idle outperforms existing approaches aiming at solving the Rule Placement Problem, averaging a 30% increase in performance.

Review of technologies for capturing carbon dioxide from industrial emissions as part of reducing the carbon footprint of plants and useful applications in water-gas stimulation techniques for enhanced oil recovery

The article discusses the importance of finding effective technological solutions for CO2 capture and subsequent utilisation in the context of underground storage or underground injection of carbon dioxide in order to implement water-gas effects to enhance hydrocarbon recovery. The authors also focus on current and promising carbon capture methods, comparing their technological features, advantages and disadvantages. Conclusions are drawn about which technological developments may be the most promising for solving the problem of reducing the carbon footprint of energy industry enterprises, and where there is a need for further research, despite the high energy costs and technological complexities.

Optimizing separate and combined grids for cost-effective hybrid renewable energy electrification in Mogadishu, Somalia

Expanding access to affordable, reliable, and sustainable electricity is an urgent priority in Somalia, which suffers from high energy costs and climate vulnerability despite negligible emissions. Therefore, this study employs MATLAB simulation software and three algorithms—particle swarm optimization (PSO), genetic algorithm, and simulated annealing—to determine optimal separate and combined grid designs for a hybrid renewable energy system in Mogadishu, Somalia. Four system configurations are assessed, namely, utilities—diesel-alone, Photovoltaic (PV)–diesel hybrid, wind–diesel hybrid, and PV–wind–diesel hybrid across the grids of three utilities—Benadir Electric Company, Mogadishu Power Supply, and Blue-Sky Energy. The algorithms evaluate technical feasibility, economic viability, and system reliability to identify the most suitable configurations. Forty-eight systems are analyzed. The combined PV/wind/diesel grid optimized with PSO is most effective, reducing the net present cost by 32%–47% and levelized cost of electricity by 41%–60% compared to diesel-only systems while maintaining reliability. Greenhouse gas emissions decrease by 38%–59% with the hybrid system.

TRENDS IN THE DEVELOPMENT OF SOIL CULTIVATION TECHNOLOGIES IN THE MOUNTAINOUS CONDITIONS OF THE REPUBLIC OF ARMENIA IN THE CONTEXT OF ENVIRONMENTAL ISSUES

The main reasons for the reduction of plowing volumes worldwide have been presented, among which, especially for the conditions of the Republic of Armenia, the high energy costs and negative environmental consequences have been highlighted particularly the large volumes of slope erosion, excessive soil compaction under tractor tracks, and the formation of a compacted plow pan layer, which negatively affects plant growth and development. Alternative soil cultivation technologies have been discussed specifically minimum and zero tillage. Several other technologies that do not have widespread application have also been considered. A comparative analysis of new and old soil cultivation technologies has been conducted. It has been clarified that the duration of scientific research related to minimum and zero tillage does not exceed 10-12 years; therefore, a logical question has arisen: is it possible to permanently apply no-till cultivation technologies? The idea of minimizing the damage inflicted on the agro-ecosystem has been set as the basis for choosing soil cultivation technologies. By combining the positive and negative aspects of different plowing and no-till cultivation technologies, an intermediate solution has been proposed: to perform plowing periodically once every 3-5 years (depending on soil-climatic conditions) and to mitigate the negative consequences of plowing, it is planned to perform it only with combined machines equipped with a plow. An environmental assessment of the proposed combined machine has been provided. For this purpose, indicators such as soil loss, mechanical and wind erosion, as well as mechanical soil degradation have been selected. For the latter, soil compaction under tractor tracks has been considered. Using the patterns developed in previous scientific research on mechanical soil degradation, it has been clarified that when using the proposed combined machine, the soil density under tractor tracks will decrease by about 0.17 g/cm³, as a result of which the yield will increase by up to 14%, and during slope cultivation (mechanical erosion) by about 60 cm. Wind erosion will also be significantly reduced.

The Ambivalence of the Liberal-Illiberal Dynamic

This essay discusses the democratic ambivalences of situations when researchers or academic institutions have to decide whom to provide a platform, where to draw red lines even at the expense of freedom of speech, whether to hide certain information, and how to label “illiberal” actors in academic pieces—questions which reflect also one’s position within academia. The article draws on the experience with the unexpected performance of an academic roundtable discussion in the East German city of Frankfurt (Oder) planned to be about weekly local street protests, which eventually was joined by the street protesters themselves. During the pandemic, these marches contested covid restrictions. With the onset of the Russian war on Ukraine, they had transformed into protests against high energy costs, in support of Putin, and against the German government. This type of protest movement resembles a typical expression of contemporary illiberalism: an ideologically heterogeneous milieu from far-right to politically conservative, if not leftist-minded people, who unite a skepticism towards the democratic state, the rejection of public media, and an affinity for conspiracy theories. The German context is particularly challenging as these “Monday demonstrations” prevailing in East Germany entail a reference to the freedom and democracy claims of the 1989 revolution, and reveal unequal conditions between East and West.

Hierarchical Model-Based Irrigation Control for Vertical Farms

As high energy costs and water scarcity remain major problems in the context of vertical farming, we present a hierarchical control system developed for the optimization of irrigation processes in these farms. The main objective is to minimize the energy cost and water consumption associated with the operation of a vertical farm, with special emphasis on irrigation in a soil-based environment. At a higher level, an optimal control problem based on a dynamic crop growth model and the FAO Penman-Monteith equations is solved offline to determine the optimal daily inputs, considering temperature, radiation, and plant available water as decision variables. At a lower level, strategically placed Proportional-Integral-Derivative (PID) controllers are used to track these irrigation set-points online. These controllers are designed using a dynamic drip irrigation and soil model that simulates the movement of water through soil. In a simulation study, this integration of set-points and subordinate PID controllers shows a robust control system that effectively stabilizes the irrigation process. The results also show a uniform moisture distribution in the soil after a short time, which contributes to uniform plant growth in the farm. The proposed hierarchical control system combines computationally intensive offline open-loop optimization for daily input determination with fast and simple online PID control for real-time stabilization.

Metabolic energetics underlying attractors in neural models.

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.

Progress on Aqueous Rechargeable Aluminium Metal Batteries

Aqueous rechargeable aluminum metal batteries (ARAMBs) take advantage of high energy density, cost efficiency and decent safety. However, the parasitic reactions between the Al anode and electrolytes, the sluggish dynamic...

Research on Solar Heating and Anti freezing Technology for Water Supply Facilities in Severe Cold Regions

This study focuses on the serious winter freezing damage, inconvenient management of coal-fired heating, and high energy consumption and cost of electric heating and antifreeze measures in rural pastoral areas in northern China's severely cold regions. Taking into account the abundant resource advantages of northern light resources, a solar heating and antifreeze system for water supply facilities was designed. During the day, the collector absorbs sunlight and converts it into heat energy. The water in the heat storage tank is circulated through the collector to heat and store energy; When the temperature of the well room is low, heat is released to stabilize the temperature of the well at around 8 °C, achieving solar heating and anti freezing. Through experimental verification, the temperature of solar powered hot water can reach 67.9 °C, and the temperature of the well room can be maintained between 6.1 °C and 10.5 °C. The heating and anti freezing effect of the system is stable, and the solar heating guarantee rate is nearly 75%. The energy-saving effect is significant, greatly reducing the anti freezing operation cost of water supply facilities. At the same time, the system can operate with full process automation and remote monitoring, effectively improving the antifreeze guarantee rate and management efficiency.

Strontium-90 pollution can be bioremediated with the green microalga Tetraselmis chui.

Strontium-90 (90Sr) is an artificial radioisotope produced by nuclear fission, with a relatively long half-life of 29 years. This radionuclide is released into the environment in the event of a nuclear incident, posing a serious risk to human and ecosystem health. There is a need to develop new efficient methods for the remediation of 90Sr, as current techniques for its removal have significant technical limitations and involve high energy and economic costs. Recently, several species of green microalgae within the class Chlorodendrophyceae have been found to form intracellular mineral inclusions of amorphous calcium carbonate (ACC), which can be highly enriched in natural (non-radiogenic) Sr. As bioremediation techniques are an attractive option to address radioactive pollution, we investigated the capacity of the unicellular alga Tetraselmis chui (class Chlorodendrophyceae) to sequester 90Sr. The 90Sr uptake capacity of T. chui cells was assessed in laboratory cultures by monitoring the time course of radioactivity in the culture medium using liquid scintillation counting (LSC). T. chui was shown to effectively sequester 90Sr, reducing the initial radioactivity of the culture medium by up to 50%. Thus, this study demonstrates the potential of the microalga T. chui to be used as a bioremediation agent against 90Sr pollution.

Toward Swimming Speed Optimization of a Multi-Flexible Robotic Fish With Low Cost of Transport

Due to the complex mechanism and fabrication process of flexible materials, it remains extremely challenging for a flexible robotic fish to achieve fast and efficient locomotion. In this article, taking advantage of the passive bending and energy storage properties of flexible materials, we propose an untethered robotic fish with multiple flexible joints to achieve high performance and low Cost of Transport (COT). First, combining rigid links and flexible materials, a compact flexible tail with a simple and efficient structure is proposed. Next, the pseudo-rigid body theory is applied to analyze the deformation of passive joints, and a full-state dynamic model is established. More importantly, an optimization method by adjusting the phase differences of the passive joints is used to obtain high aquatic performance. Finally, extensive simulations and experiments validate the effectiveness of the proposed method, and the robotic fish can achieve a maximum speed of 1.63 body length (BL) per second and a minimum COT of 4.8 J/m (2.87 J/m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot$</tex-math> </inline-formula> kg). Compared with the multi-joint robotic fish with a similar design, the COT is reduced by up to 81.05% with the basically same aquatic ability. Excitingly, the flexible robotic fish can achieve a COT of 7.36 J/m at 1.23 BL/s, which is 15.72%-36.34% lower than that of the bluefin tuna and is within the range of yellowfin tuna, offering valuable insight into high speed and long endurance applications for underwater robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> –This paper is motivated by the design and optimization of an efficient bionic flexible underwater robot with high aquatic abilities, which is conducive to aquatic tasks that require long-time and long-distance sailing, such as underwater topographic exploration, submarine archaeology, and underwater search and rescue. Existing studies of free-swimming bionic underwater robots usually focus on the improvement of swimming speed and rarely consider achieving both high swimming performance and low energy cost. Thus, this paper proposes a bionic underwater robot design with two joints made of flexible materials on the tail to address the problem. Based on detailed analyses of the hydrodynamic force and flexible joint deformation, we propose an effective optimization method for swimming performance. A series of simulations and experiments suggest that the mechatronic design and optimization method are practical and valid. Hopefully, our design and method can provide theoretical guidance for engineers to design and optimize robots with flexible joints.